CP-CHARM: Segmentation-Free Image Classification Made Accessible

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The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Edge-AI applications necessitate the joint application of hardware acceleration and software optimization to meet energy and area constraints. However, the co-design of these systems is hindered by the lack of integration options for novel hardware prototy ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

The polarization of tau leptons is measured using leptonic and hadronic tau lepton decays in Z -> tau(+)tau(-) events in proton-proton collisions at root s = 13 TeV recorded by CMS at the CERN LHC with an integrated luminosity of 36.3 fb(-1). The measured ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astro ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Subjective interpretation and content diversity make predicting whether an image is private or public a challenging task. Graph neural networks combined with convolutional neural networks (CNNs), which consist of 14,000 to 500 millions parameters, generate ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

CP-CHARM: Segmentation-Free Image Classification Made Accessible

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Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Machine Learning for Modeling Stock Returns

HEEPstor: an Open-Hardware Co-design Framework for Quantized Machine Learning at the Edge

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Image steganalysis using active learning and hyperparameter optimization

Measurement of the τ lepton polarization in Z boson decays in proton-proton collisions at √s=13 TeV

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Partial discharge localization in power transformer tanks using machine learning methods

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Learning Privacy from Visual Entities

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Random matrix methods for high-dimensional machine learning models