In this paper, we experimentally investigate how the 3D sound localization capabilities of the blind can improve through perceptual training. To this end, we develop a novel perceptual training method with sound-guided feedback and kinesthetic assistance to evaluate its effectiveness compared to conventional training methods. In perceptual training, we exclude visual perception by blindfolding the subjects to apply the proposed method to the visually impaired. Subjects used a specially designed pointing stick to generate a sound at the tip, indicating localization error and tip position. The proposed perceptual training aims to evaluate the training effect on 3D sound localization, including variations in azimuth, elevation, and distance. The six days of training based on six subjects resulted in the following outcomes: (1) In general, accuracy in full 3D sound localization can be improved based on training. (2) Training based on relative error feedback is more effective than absolute error feedback. (3) Subjects tend to underestimate distance when the sound source is near, less than 1000 mm, or larger than 15° to the left, and overestimate the elevation when the sound source is near or in the center, and within ±15° in azimuth estimations.
Résumé Dans cet article nous présentons une réflexion sémiotique sur la représentation de l’humain et du non-humain dans des discours de techno-fiction reflétant l’humanité transitoire en relation avec le progrès technoscientifique. Les signifiants dénotatifs dans le contexte narratif nous conduisent à observer et à analyser les signifiés connotatifs. C’est dans cet esprit que nous examinerons des scènes tirées du film de science-fiction coréen La Créature céleste portant sur l’existence de l’humain, et son rapport ontologique avec la technologie. Ce film unique en son genre traitant à la fois de la machine et de la spiritualité dans la tradition du bouddhisme s’appuie sur les principes fondamentaux du Mahayana (Grand Véhicule) comme la vacuité, l’éveil et la nature-de-bouddha et propose, avec un bouddha robot et un homme-machine, une représentation bien particulière de l’humanité en transition. Ce film contient diverses propositions vis-à-vis d’un phénomène mystérieux et problématique, produit par une technologie future. Nous proposons d’analyser les différentes significations telles que le dualisme du corps et de l’esprit, un fantasme permettant surmonter la limite physique par le recours à la technologie, une inscription corporelle de l’esprit et l’interaction dans l’environnement technoscientifique. Il est possible d’appliquer cette interprétation aux autres discours de techno-fiction et proposer une lecture et une écriture qui peuvent nous guider dans notre réflexion sur l’avenir, sans peur ni enthousiasme, mais selon la notion de vacuité. Cet article est basé sur des communications délivrées à l’occasion de deux colloques internationaux organisés dans le cadre d’une collaboration entre deux équipes de chercheurs coréens et français, projet soutenu par le LABEX ARTS-H2H au titre du programme, Investissements d’avenir .
Abstract We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.
"Being Mondrian" is a public installation aimed for interactive drawing utilizing a novel tangible interface. Abstract painting has been conceived as a monopoly permitted to professional artists with special techniques. However, at the same time, such art works are so apparently simple that ordinary people dare to think to simulate them.
CNN 기반의 악성코드 탐지모델을 활용하기 위해 다양한 이미지 형식을 사용할 수 있다. 하지만 대부분의 기존 연구들은 최종적인 악성코드 탐지 및 분류 성능을 주로 강조하고 있으며, CNN에 입력되는 이미지의 형식이 모델의 성능과 자원 사용 량에 미칠 수 있는 영향은 거의 고려하지 않는다. 이에 본 논문에서는 CNN을 기반으로 안드로이드 악성코드를 탐지하는 모 델을 구축함에 있어 입력되는 이미지 형식이 탐지성능과 학습에 소요되는 자원의 사용량에 어떠한 영향을 미치는지를 분석하 였다. CICAndMal2017 데이터세트를 사용하여 BMP, JPG, PNG 및 TIFF 4가지 형식의 이미지로 변환하고, 자체적으로 구축 한 CNN 모델에 학습시킨 후 악성코드 탐지성능과 자원 사용량을 측정하였다. 그 결과 이미지 형식에 따른 이진분류 및 다중 분류 성능과 GPU 및 RAM 사용량은 큰 차이를 보이지 않았다. 그러나 생성된 이미지의 파일 크기는 이미지 형식에 따라 최 대 6배까지 차이가 났으며, 학습에 소요되는 시간에서도 유의미한 차이가 발생함을 확인하였다.
Objective To determine if there are sex differences in myelin in Parkinson’s disease, and whether these explain some of the previously-described sex differences in clinical presentation. Methods Thirty-three subjects (23 males, 10 females) with Parkinson’s disease underwent myelin water fraction (MWF) imaging, an MRI scanning technique of in vivo myelin content. MWF of 20 white matter regions of interest (ROIs) were assessed. Motor symptoms were assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS). Principal component analysis, logistic and multiple linear regressions, and t-tests were used to determine which white matter ROIs differed between sexes, the clinical features associated with these myelin changes, and if overall MWF and MWF laterality differed between males and females. Results Consistent with prior reports, tremor and bradykinesia were more likely seen in females, whereas rigidity and axial symptoms were more likely seen in males in our cohort. MWF of the thalamic radiation, cingulum, cingulum hippocampus, inferior fronto-occipital fasciculi, inferior longitudinal fasciculi, and uncinate were significant in predicting sex. Overall MWF and asymmetry of MWF was greater in males. MWF differences between sexes were associated with tremor symptomatology and asymmetry of motor performance. Conclusion Sex differences in myelin are associated with tremor and asymmetry of motor presentation. While preliminary, our results suggest that further investigation of the role of biological sex in myelin pathology and clinical presentation in Parkinson’s disease is warranted.
Parkinson's disease (PD), characterized by slowness of movement, tremor and rigidity, is one of the most prevalent neurodegenerative disorders. Recent studies have demonstrated that abnormal neural oscillations within and between multiple brain regions play a critical role in the motor symptoms through invasive neural recordings. Progressions have been also made in EEG studies to use features in cortical oscillations recorded non-invasively as a diagnostic tool for PD. However, it is still challenging to effectively use EEG recordings for PD diagnosis. In this work, we design a novel deep learning framework for PD EEG classification. Specifically, the convolutional neural network (CNN) and the recurrent neural network (RNN) with long shortterm memory (LSTM) cells are exploited in our framework. First, we design two 1D-CNN layers to derive features to represent spatial (topological) relationships across EEG channels. Then, we apply LSTM on the spatial features from the CNN to further improve its performance. Finally, we validate our model on the PD classification on resting EEG recorded from 20 PD and 21 healthy subjects. Our method achieves accuracy of 96.9%, precision of 100%, and recall of 93.4% for differentiating PD from healthy controls and outperforms the state-of-the-art PD EEG classification results in the deep learning literature.
Electroencephalography (EEG) is an important noninvasive neural recording technique with a broad application in the field of neurological instrumentation and measurement. However, EEG signals are often contaminated by muscle artifacts, adversely affecting the subsequent analysis. Joint blind source separation (JBSS) models have been successfully applied to remove muscle artifacts from EEG recordings, although most of them were designed for EEG collected in well-controlled conditions. Without considering the dynamics of underlying mixtures in complex environments may hinder the real mobile and long-term healthcare monitoring. To deal with such concern, we assume that the mixing process of sources dynamically changes over time and propose a state-dependent JBSS model by integrating the hidden Markov model with independent vector analysis in a maximum likelihood framework. It is capable of identifying the varying sources of muscle artifact components and underlying EEG signals. The proposed method was evaluated on both simulated and semi-simulated data, and demonstrated superior performance compared with other popular approaches for muscle artifacts removal in dynamic environments. The state-dependent JBSS model provides a novel way to investigate the temporal dynamics of multiple multidimensional biomedical data sets simultaneously.
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