Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1. 26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Blood oxygen saturation (SpO$_2$) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic.
Second, we design an iterative self-training strategy to align the fine-grained class distributions for the source and target domains via target domain pseudo labels.
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
Ranked #1 on Recognizing And Localizing Human Actions on HAR
In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces.
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on Automatic Sleep Stage Classification on Sleep-EDF
In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
Energy landscape analysis is a method that can be used to quantify these dynamics.
The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring.
This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.
We analyze the latent connection between PPG and ECG as well as the CVDs-related features of PPG learned by the neural network, aiming at obtaining clinical insights from data.
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.
Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.
The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters.
This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold.
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.
In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals.
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation.
Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos.
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples.
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks.
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations.
In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples.
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.
Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations.
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.
Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime.
We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate.
For the boosting-like strategy, we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers.
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.