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Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities

SENSORS 2021年第12期21卷 4133-4133页

作者： Heidarivincheh, Farnoosh McConville, Ryan Morgan, Catherine McNaney, Roisin Masullo, Alessandro Mirmehdi, Majid Whone, Alan L. Craddock, Ian Univ Bristol Sch Comp Sci Elect & Elect Engn & Engn Maths Bristol BS8 1UB Avon England Univ Bristol Med Sch Translat Hlth Sci Bristol BS8 1UD Avon England North Bristol NHS Trust Bristol Brain Ctr Bristol BS10 5PN Avon England Monash Univ Dept Human Centred Comp Melbourne Vic 3000 Australia

Parkinson's disease (PD) is a chronic neurodegenerative condition that affects a patient's everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.

关键词： Parkinson's disease deep learning multimodal data missing modality accelerometer computer vision variational autoencoder

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Improving calibration of forensic glass comparisons by considering uncertainty in feature-based elemental data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 217卷 104399-104399页

作者： Ramos, Daniel Maronas, Juan Almirall, Jose Univ Autonoma Madrid Escuela Politecn Super AUDIAS Lab Aud Data Intelligence & Speech Calle Francisco Tomas & Valiente 11 Madrid 28049 Spain Univ Politecn Valencia Escuela Tecn Super Ingn Informat Pattern Recognit & Human Language Tecnol PRHLT Re Camino Vera S-N Valencia 46022 Spain Florida Int Univ Dept Chem & Biochem Ctr Adv Res Forens Sci Miami FL 33199 USA

The computation of likelihood ratios (LR) to measure the weight of forensic glass evidence with LA-ICP-MS data directly in the feature space without computing any kind of score as an intermediate step is a complex problem. A probabilistic two-level modeling of the within-source and between-source variability of the glass samples is needed in order to compare the elemental profiles measured from glass recovered from a suspect or a crime scene and compared to glass samples of a known source of origin. Calibration of the likelihood ratios generated using previously reported models is essential to the realistic reporting of the value of the glass evidence comparisons. We propose models that outperform previously proposed feature-based LR models, in particular by improving the calibration of the computed LRs. We assume that the within-source variability is heavy-tailed, in order to incorporate uncertainty when the available data is scarce, as it typically happens in forensic glass comparison. Moreover, we address the complexity of the between-source variability by the use of probabilistic machine learning algorithms, namely a variational autoencoder and a warped Gaussian mixture. Our results show that the overall performance of the likelihood ratios generated by our model is superior to classical approaches, and that this improvement is due to a dramatic improvement in the calibration despite some loss in discriminating power. Moreover, the robustness of the calibration of our proposal is remarkable.

关键词： Likelihood ratio Forensic glass comparison LA-ICP-MS variational autoencoder Warped Gaussian mixture Heavy-tailed

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VASP: An autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104354-104354页

作者： von Schleinitz, Julian Graf, Michael Trutschnig, Wolfgang Schroeder, Andreas BMW AG Motorsport Munich Germany Univ Salzburg Salzburg Austria

The aim is to provide a framework for robust time series prediction in the presence of anomalies. The framework is developed based on a data set from motorsport but is not limited to this specific area. In motorsport, the usage of sensors during races is generally restricted. Estimating the outputs of these missing sensors therefore provides an advantage over the competition. Deep learning approaches such as long short-term memory (LSTM) neural networks have proven to be useful for that task, however, their accuracy decreases significantly if anomalies occur in the input signals. To overcome this problem, we propose the variational autoencoder based selective prediction (VASP) framework which combines the tasks of anomaly detection and time series prediction. VASP consists of a variational autoencoder (VAE), an anomaly detector and LSTM predictors. Depending on the anomaly detector, a subset of the inputs may be replaced by the VAE, allowing a more robust prediction. To the best of our knowledge the approach of using a VAE to only selectively replace anomalous input data before prediction has not yet been published. Our contributions are clear implementation guidelines and a comparison to other VAE-based methods and a LSTM approach as baseline. We simulate anomalies with three approaches and show that VASP outperforms other methods by having no trade-off between accuracy and robustness. VASP is as accurate as the baseline for regular data, but for anomalous inputs the error is reduced by 13% to 33% on average and up to 70% in special cases.

关键词： variational autoencoder Anomaly detection Time series prediction Motorsport Deep learning LSTM

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Human Laughter Generation using Hybrid Generative Models

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2021年第5期15卷 1590-1609页

作者： Mansouri, Nadia Lachiri, Zied El Manar Univ Natl Engn Sch Tunisia Tunis 1002 Tunisia

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

关键词： Laughter Synthesis variational autoencoder autoencoder Objective and Subjective Evaluation

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ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution

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JOURNAL OF NEURAL ENGINEERING 2021年第4期18卷

作者： Rokai, Janos Racz, Melinda Fiath, Richard Ulbert, Istvan Marton, Gergely Res Ctr Nat Sci Inst Cognit Neurosci & Psychol Magyar Tudosok Korutja 2 H-1117 Budapest Hungary Semmelweis Univ Sch PhD Studies Ulloi Ut 26 H-1085 Budapest Hungary Pazmany Peter Catholic Univ Fac Informat Technol & Bion Prater Utca 50-A H-1083 Budapest Hungary

Objective. The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting. Approach. In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion. Main results. The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F-1 scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points). Significance. ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.

关键词： spike sorting deep learning variational autoencoder

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Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model

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COMPUTATIONAL MATERIALS SCIENCE 2021年 186卷 110018-110018页

作者： Zhang, Fan Teng, Qizhi Chen, Honggang He, Xiaohai Dong, Xiucheng Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu Peoples R China Minist Educ Key Lab Wireless Power Transmiss Chengdu Peoples R China

Three-dimensional (3D) microstructures are useful for studying the spatial structures and physical properties of porous media. A number of stochastic reconstructions are essential to discover the geometry and topology of the porous media and the its flow behavior. While several deep-learning based generative models have been proposed to deal with the issue, the obstacles about stable training and difficulty in convergence limit the application of these models. To address these problems, a hybrid deep generative model for 3D porous media reconstruction is proposed. The hybrid model is composed of a variant autoencoder (VAE) and a generative adversarial network (GAN). It receives a two-dimensional image as input and generates 3D porous media. The encoder from VAE characterizes the statistical and morphological information of input image and generates a low-dimensional feature vector for generator. Benefiting from the hybrid model, the training becomes more stable and the generative capability is enhanced as well. Furthermore, a simple but useful loss function is used to help improve accuracy. The proposed model is tested on both isotropic and anisotropic porous media. The results show the synthetic realizations have good agreement to the targets on visual inspection, statistical functions and two-phase flow simulation.

关键词： Porous media 3D reconstruction Generative adversarial networks variational autoencoder Hybrid deep generative model

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Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach

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OPHTHALMOLOGY GLAUCOMA 2020年第3期3卷 210-217页

作者： Asaoka, Ryo Murata, Hiroshi Matsuura, Masato Fujino, Yuri Yanagisawa, Mieko Yamashita, Takehiro Univ Tokyo Dept Ophthalmol Grad Sch Med Tokyo Japan Univ Tokyo Fac Med Tokyo Japan Kitasato Univ Grad Sch Med Sci Dept Ophthalmol Sagamihara Kanagawa Japan Kagoshima Univ Dept Ophthalmol Grad Sch Med & Dent Sci Kagoshima Japan

Purpose: To investigate whether processing visual field (VF) measurements using a variational autoencoder (VAE) improves the structure-function relationship in glaucoma. Design: Cross-sectional study. Participants: The training data consisted of 82 433 VF measurements from 16 836 eyes. The testing dataset consisted of 117 eyes of 75 patients with open-angle glaucoma. Methods: A VAE model to reconstruct the threshold of VF was developed using the training dataset. OCT and VF (Humphrey Field Analyzer 24-2, Swedish interactive threshold algorithm standard) measurements were carried out for all eyes in the testing dataset. Visual fields in the testing dataset then were reconstructed using the trained VAE. The structure-function relationship between the circumpapillary retinal nerve fiber layer (cpRNFL) thickness and VF sensitivity was investigated in each of twelve 30 degrees segments of the optic disc (3 nasal sectors were merged). Similarly, the structure-function relationship was investigated using the VAE-reconstructed VF. Main Outcome Measures: Structure-function relationship. Results: The corrected Akaike information criterion values with threshold were found to be smaller than the threshold reconstructed with the VAE (threshold(VAE)) in 9 of 10 sectors. A significant relationship was found between threshold and cpRNFL thickness in 6 of 10 sectors, whereas it was significant in 9 of 10 sectors with threshold(VAE). Conclusions: Applying VAE to VF data results in an improved structure-function relationship. (C) 2020 by the American Academy of Ophthalmology

关键词： AICc corrected Akaike information criterion cpRNFL circumpapillary retinal nerve fiber layer HFA Humphrey Field Analyzer threshold VAE threshold reconstructed with the variational autoencoder VAE variational autoencoder VF visual field

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A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network

A Generative Learning Approach for Spatio-temporal Modeling ...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Xia, Rong Xiao, Yong Li, Yingyu Krunz, Marwan Niyato, Dusit Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Hubei Peoples R China Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781728150895

Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems. The quality of the molded results rely heavily on the number and quality of samples which can vary significantly due to the sensor deployment density as well as traffic volume and density. This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive spatio-temporal of wireless access latency of a connected vehicles across a wide geographical area. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a variational autoencoder (VAE), a deep generative model, to create latency samples with similar probability distribution with the original samples. Finally, LaMI establishes the empirical PDF of latency performance and maps the PDFs into the confidence levels of different vehicular service requirements. Extensive performance evaluation has been conducted using the real traces collected in a commercial LTE network in a university campus. Simulation results show that our proposed model can significantly improve the accuracy of latency modeling especially compared to existing popular solutions such as interpolation and nearest neighbor-based methods.

关键词： Connected vehicle latency modeling variational autoencoder C-V2X

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CROSS-VAE: TOWARDS DISENTANGLING EXPRESSION FROM IDENTITY FOR HUMAN FACES

CROSS-VAE: TOWARDS DISENTANGLING EXPRESSION FROM IDENTITY FO...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wu, Haozhe Jia, Jia Xie, Lingxi Qi, Guojun Shi, Yuanchun Tian, Qi Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Futurewei Technol Plano TX USA

ISBN: (纸本)9781509066315

Facial expression and identity are two independent yet intertwined components for representing a face. For facial expression recognition, identity can contaminate the training procedure by providing tangled but irrelevant information. In this paper, we propose to learn clearly disentangled and discriminative features that are invariant of identities for expression recognition. However, such disentanglement normally requires annotations of both expression and identity on one large dataset, which is often unavailable. Our solution is to extend conditional VAE to a crossed version named Cross-VAE, which is able to use partially labeled data to disentangle expression from identity. We emphasis the following novel characteristics of our Cross-VAE: (1) It is based on an independent assumption that the two latent representations' distributions are orthogonal. This ensures both encoded representations to be disentangled and expressive. (2) It utilizes a symmetric training procedure where the output of each encoder is fed as the condition of the other. Thus two partially labeled sets can be jointly used. Extensive experiments show that our proposed method is capable of encoding expressive and disentangled features for facial expression. Compared with the baseline methods, our model shows an improvement of 3.56% on average in terms of accuracy.

关键词： Facial expression recognition Disentangle variational autoencoder

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Privacy-Preserving Data Generation and Sharing Using Identification Sanitizer 21st

Privacy-Preserving Data Generation and Sharing Using Identif...

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21st International Conference on Web Information Systems Engineering (WISE)

作者： Wang, Shuo Lyu, Lingjuan Chen, Tianle Chen, Shangyu Nepal, Surya Rudolph, Carsten Grobler, Marthie CSIRO Data61 Melbourne Vic Australia Monash Univ Melbourne Vic Australia Natl Univ Singapore Singapore Singapore Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9783030620080;9783030620073

In this paper, we propose a practical privacy-preserving generative model for data sanitization and sharing, called Sanitizer-variational autoencoder (SVAE). We assume that the data consists of identification-relevant and irrelevant components. A variational autoencoder (VAE) based sanitization model is proposed to strip the identification-relevant features and only retain identification-irrelevant components in a privacy-preserving manner. The sanitization allows for task-relevant discrimination (utility) but minimizes the personal identification information leakage (privacy). We conduct extensive empirical evaluations on the real-world face, biometric signal and speech datasets, and validate the effectiveness of our proposed SVAE, as well as the robustness against the membership inference attack.

关键词： Generative model Data sharing Privacy-preserving Deep learning variational autoencoder

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