896 research outputs found
Through-Wall Imaging based on WiFi Channel State Information
This work presents a seminal approach for synthesizing images from WiFi
Channel State Information (CSI) in through-wall scenarios. Leveraging the
strengths of WiFi, such as cost-effectiveness, illumination invariance, and
wall-penetrating capabilities, our approach enables visual monitoring of indoor
environments beyond room boundaries and without the need for cameras. More
generally, it improves the interpretability of WiFi CSI by unlocking the option
to perform image-based downstream tasks, e.g., visual activity recognition. In
order to achieve this crossmodal translation from WiFi CSI to images, we rely
on a multimodal Variational Autoencoder (VAE) adapted to our problem specifics.
We extensively evaluate our proposed methodology through an ablation study on
architecture configuration and a quantitative/qualitative assessment of
reconstructed images. Our results demonstrate the viability of our method and
highlight its potential for practical applications
Active Learning in Physics: From 101, to Progress, and Perspective
Active Learning (AL) is a family of machine learning (ML) algorithms that
predates the current era of artificial intelligence. Unlike traditional
approaches that require labeled samples for training, AL iteratively selects
unlabeled samples to be annotated by an expert. This protocol aims to
prioritize the most informative samples, leading to improved model performance
compared to training with all labeled samples. In recent years, AL has gained
increasing attention, particularly in the field of physics. This paper presents
a comprehensive and accessible introduction to the theory of AL reviewing the
latest advancements across various domains. Additionally, we explore the
potential integration of AL with quantum ML, envisioning a synergistic fusion
of these two fields rather than viewing AL as a mere extension of classical ML
into the quantum realm.Comment: 15 page
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Deep generative models,
particularly, Generative Adversarial Networks (GANs) show great promise in
generating synthetic EHR data by learning underlying data distributions while
achieving excellent performance and addressing these challenges. This work aims
to review the major developments in various applications of GANs for EHRs and
provides an overview of the proposed methodologies. For this purpose, we
combine perspectives from healthcare applications and machine learning
techniques in terms of source datasets and the fidelity and privacy evaluation
of the generated synthetic datasets. We also compile a list of the metrics and
datasets used by the reviewed works, which can be utilized as benchmarks for
future research in the field. We conclude by discussing challenges in GANs for
EHRs development and proposing recommended practices. We hope that this work
motivates novel research development directions in the intersection of
healthcare and machine learning
A survey of generative adversarial networks for synthesizing structured electronic health records
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning
Bayesian inference of the spatial distributions of material properties
The inverse problem of estimating the spatial distributions of elastic material properties from noisy strain measurements is ill-posed. However, it is still typically treated as an optimisation problem to maximise a likelihood function that measures the agreement between the measured and theoretically predicted strains. Here we propose an alternative approach employing Bayesian inference with Nested Sampling used to explore parameter space and compute Bayesian evidence. This approach not only aids in identifying the basis function set (referred to here as a model) that best describes the spatial material property distribution but also allows us to estimate the uncertainty in the predictions. Increasingly complex models with more parameters generate very high likelihood solutions and thus are favoured by a maximum likelihood approach. However, these models give poor predictions of the material property distributions with a large associated uncertainty as they overfit the noisy data. On the other hand, the Bayes’ factor peaks for a relatively simple model and indicates that this model is most appropriate even though its likelihood is comparatively low. Intriguingly, even for the appropriate model that has a unique maximum likelihood solution, the measurement noise is amplified to give large errors in the predictions of the maximum likelihood solution. By contrast, the mean of the posterior probability distribution reduces the effect of noise in the data and predicts the material properties with significantly higher fidelity. Simpler model selection criteria such as the Bayesian information criterion are shown to fail due to the non-Gaussian nature of the posterior distribution of the parameters. This makes accurate evaluation of the posterior distribution and the associated Bayesian evidence integral (by Nested Sampling or other means) imperative for this class of problems. The output of the Nested Sampling algorithm is also used to construct likelihood landscapes. These landscapes show the existence of multiple likelihood maxima when there is paucity of data and/or for overly complex models. They thus graphically illustrate the pitfalls in using optimisation methods to search for maximum likelihood solutions in such inverse problems.Royal Societ
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