We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in ...the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification.
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and ...often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories.
We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide a great deal of ...sequencing data thanks to their high throughput, but the associated error rates often tend to be high. Denoising in high-throughput sequencing has thus become a crucial process for boosting the reliability of downstream analyses. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and effectively corrects substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput targeted amplicon sequencing platforms. Our experimental studies with real and simulated datasets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capability and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables its robust application to different sequencing platforms and analysis pipelines by simple updates of the noise model. DUDE-Seq is available at http://data.snu.ac.kr/pub/dude-seq.
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Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on
...using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the
DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.
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We consider class incremental learning (CIL) problem, in which a learning agent continuously learns new classes from incrementally arriving training data batches and aims to predict well on all the ...classes learned so far. The main challenge of the problem is the catastrophic forgetting, and for the exemplar-memory based CIL methods, it is generally known that the forgetting is commonly caused by the classification score bias that is injected due to the data imbalance between the new classes and the old classes (in the exemplar-memory). While several methods have been proposed to correct such score bias by some additional post-processing, e.g., score re-scaling or balanced fine-tuning, no systematic analysis on the root cause of such bias has been done. To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main cause of the bias. Then, we propose a new method, dubbed as Separated Softmax for Incremental Learning (SS-IL), that consists of separated softmax (SS) output layer combined with task-wise knowledge distillation (TKD) to resolve such bias. Throughout our extensive experimental results on several large-scale CIL benchmark datasets, we show our SS-IL achieves strong state-of-the-art accuracy through attaining much more balanced prediction scores across old and new classes, without any additional post-processing.
Objective
To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction.
...Methods
T2‐weighted oblique coronal images of the brain MRI epilepsy protocol performed on patients were used. The training set included 320 participants with 160 no, 100 left and 60 right hippocampal sclerosis, and cross‐validation was implemented. The test set consisted of 302 participants with 252 no, 25 left and 25 right hippocampal sclerosis. As the test set was imbalanced, we took an average of the accuracy achieved within each group to measure a balanced accuracy for multiclass and binary classifications. The dataset was composed to include not only healthy participants but also participants with abnormalities besides hippocampal sclerosis in the control group. We visualized the reasons for the model prediction using the layer‐wise relevance propagation method.
Results
When evaluated on the validation of the training set, we achieved multiclass and binary classification accuracy of 87.5% and 88.8% from the voting ensemble of six models. Evaluated on the test sets, we achieved multiclass and binary classification accuracy of 91.5% and 89.76%. The distinctly sparse visual interpretations were provided for each individual participant and group to suggest the contribution of each input voxel to the prediction on the MRI.
Significance
The current interpretable deep learning‐based model is promising for adapting effectively to clinical settings by utilizing commonly used data, such as MRI, with realistic abnormalities faced by neurologists to support the diagnosis of hippocampal sclerosis with plausible visual interpretation.
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Satellite-retrieved aerosol optical depth (AOD) can potentially provide an effective way to complement the spatial coverage limitation of a ground particulate air-pollution monitoring network such as ...the U.S. Environment Protection Agency's regulatory monitoring network. One of the current state-of-the-art AOD retrieval methods is the National Aeronautics and Space Administration's Multiangle Imaging SpectroRadiometer (MISR) operational algorithm, which has a spatial resolution of 17.6 km × 17.6 km. Although the MISR's aerosol products lead to exciting research opportunities to study particle composition at a regional scale, its spatial resolution is too coarse for analyzing urban areas, where the air pollution has stronger spatial variations and can severely impact public health and the environment. Accordingly, a novel AOD retrieval algorithm with a resolution of 4.4 km × 4.4 km has been recently developed, which is based on hierarchical Bayesian modeling and the Monte Carlo Markov chain (MCMC) inference method. In this paper, we carry out detailed quantitative and qualitative evaluations of the new algorithm, which is called the HB-MCMC algorithm, using recent AErosol RObotic NETwork (AERONET) Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign data obtained in the summer of 2011. These data, which were not available in a previous study, contain spatially dense ground measurements of the AOD and other aerosol particle characteristics from the Baltimore-Washington, DC region. Our results show that the HB-MCMC algorithm has 16.2% more AOD retrieval coverage and improves the root-mean-square error by 38.3% compared with the MISR operational algorithm. Our detailed analyses with various metrics show that the improvement of our scheme is coming from the novel modeling and inference method. Furthermore, the map overlay of the retrieval results qualitatively confirms the findings of the quantitative analyses.
Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their huge success in ...image classification, the number of images to be mixed has not been elucidated in the literature: only the naive K-image expansion has been shown to lead to performance degradation. This study derives a new K-image mixing augmentation based on the stick-breaking process under Dirichlet prior distribution. We demonstrate superiority of our K-image expansion augmentation over conventional two-image mixing augmentation methods through extensive experiments and analyses: (1) more robust and generalized classifiers; (2) a more desirable loss landscape shape; (3) better adversarial robustness. Moreover, we show that our probabilistic model can measure the sample-wise uncertainty and boost the efficiency for network architecture search by achieving a 7-fold reduction in the search time.
The problem of discrete universal filtering, in which the components of a discrete signal emitted by an unknown source and corrupted by a known discrete memoryless channel (DMC) are to be causally ...estimated, is considered. A family of filters are derived, and are shown to be universally asymptotically optimal in the sense of achieving the optimum filtering performance when the clean signal is stationary, ergodic, and satisfies an additional mild positivity condition. Our schemes are comprised of approximating the noisy signal using a hidden Markov process (HMP) via maximum-likelihood (ML) estimation, followed by the use of the forward recursions for HMP state estimation. It is shown that as the data length increases, and as the number of states in the HMP approximation increases, our family of filters attains the performance of the optimal distribution-dependent filter. An extension to the case of channels with memory is also established.
For higher density of transistors in Integrated Circuit (IC), various scaling technologies have been introduced. In the light of the physical limit in advancing single-gate transistor architecture, ...the structural transition from planar device architecture toward 3D device architecture (of which the representative one is Fin-shaped Field-Effect Transistor, or FinFET) manifests itself. However, during fabrication, the unexpected process-induced random variations of the transistor's electrical characteristics have become more extreme with aggressively scaling down the physical dimension of transistor as well as with evolving from 2D to 3D device structure. Consequently, accurate and rapid estimation of the random variations conditioned on the variation sources (e.g., LER, RDF, and WFV) is required. Recently, machine learning-based approaches were utilized to estimate the LER-induced variations, but they were highly dependent on modeling and evaluation assumptions (e.g., Gaussian or independence). To that end, firstly, we introduce a GAN-based framework for the estimation of process-induced random variations. Since GAN is free from distributional assumptions, this enables precise prediction and, more importantly, enables unified estimation, i.e., adaptable to various variation sources. Secondly, to achieve better generalization on unseen conditions, we additionally suggest a two-step learning strategy utilizing the latest Conditional GAN models. Thirdly, we introduce sample-based evaluation procedure which measures the difference between two sample sets from a probabilistic perspective. Finally, the evaluation results on LER and RDF/WFV datasets show that our GAN-based framework is computationally efficient and is able to generate synthetic samples similar to the TCAD simulated samples that contain random variations, both qualitatively and quantitatively. From such results, our GAN-based framework is expected to be successfully applied to real data, and consequently be able to reliably estimate the random variations of fabricated transistors with multiple orders of magnitude speed-up compared to the conventional TCAD simulation-based estimation.
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