Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

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Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomal ...

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

Chattez avec Graph Search

FADE: Forecasting for anomaly detection on ECG

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

Multi-Objective Management for Many-Core Computing Servers

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Discovering meaningful units from text sequences

Data-Driven Kinematic Modeling of Physical Origami Robots

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Intraday solar irradiance forecasting using public cameras

SuperAnimal pretrained pose estimation models for behavioral analysis

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Explainable ensemble learning model for predicting steel section-concrete bond strength

Learning to See and Compute through Multimode Fibers