Compressed Distributed Regression over Adaptive Networks

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Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Access to water and sanitation are major issues for many West African cities, especially for low- and middle-income nations like Mauritania. This study investigates the socioeconomic, demographic, and geographical factors influencing household water access ...

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

The deterministic errors of an accelerometer comprise the prevailing i) bias, ii) scale factor, and iii) non-orthogonality. Together, these errors result in a nonlinear measurement model, which is conventionally solved via an iterative nonlinear least-squa ...

Improper use of chemical fertilizers has led to land degradation. Organic crops, which are farmed using organic fertilizers to reduce the negative environmental effects, are considered an alternative solution to develop the ecosystem health and improve the ...

Coral reefs around the world are under threat from anomalous heat waves that are causing the widespread decline of hard corals. Different coral taxa are known to have different sensitivities to heat, although variation in susceptibilities have also been ob ...

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

This paper presents an empirical case study on applying game-based learning in an undergraduate finance course. The paper describes the experimental study context, protocol, and results. Using multivariate regression analysis, a significant game effect on ...

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, w ...

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a difficult task especially when the input dimension is high. In this letter, we ...

The rapid development of digital imaging and video has placed visual contents in the heart of our lives. Digital multimedia span a vast number of areas from business to leisure, including but not limited to education, medicine, accessibility, training, adv ...

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

Background and Purpose- Poststroke fatigue affects a large proportion of stroke survivors and is associated with a poor quality of life. In a recent trial, modafinil was shown to be an effective agent in reducing poststroke fatigue; however, not all patien ...

A promising pathway to follow in order to reach sustainable development goals is an increased reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the dem ...

Increasing meteorological data availability and quality implies an adaptation of the interpolation methods for data combination. In this paper, we propose a new method to efficiently combine weather radar data with data from two heated rain gauge networks ...

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To f ...

In this study, we evaluate established and newly developed metrics for predicting glare using data from three different research studies. The evaluation covers two different targets: 1. How well the user’s perception of glare magnitude correlates to the pr ...

Compressed Distributed Regression over Adaptive Networks

Chattez avec Graph Search

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Water for all: determinants of household water access in Nouakchott, Mauritania

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Linear Estimation of Deterministic Accelerometer Errors

Economic and commercial analysis of reusing dam reservoir sediments

Worldwide analysis of reef surveys sorts coral taxa by associations with recent and past heat stress

Adaptive hot water production based on Supervised Learning

Learning Finance with Games: An Empirical Study

The value of human data annotation for machine learning based anomaly detection in environmental systems

Efficient implementation of atom-density representations

NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates

Image Compression and Quality Assessment using Convolutional Neural Networks

Electron density learning of non-covalent systems

Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potential

Spatial interpolation of precipitation from multiple rain gauge networks and weather radar data for operational applications in Alpine catchments

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks

COMPARISON OF LUMINANCE BASED METRICS IN DIFFERENT LIGHTING CONDITIONS

Graph Transform Learning for Image Compression