robotic hand exoskeleton has been proposed to train the paretic hand after stroke, and its movements can be controlled by recognized activities of non-paretic hand. Although a variety of myoelectric pattern identifica...
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Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, ...
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Aberrant neural oscillations hallmark the pathophysiology of numerous neurological and psychiatric disorders. Here, we first report a method to accurately track the phase of neural oscillations in real-time by a Hilbe...
Aberrant neural oscillations hallmark the pathophysiology of numerous neurological and psychiatric disorders. Here, we first report a method to accurately track the phase of neural oscillations in real-time by a Hilbert transform that avoids the characteristic Gibbs distortion at the end of the signal, aka endpoint-corrected Hilbert transform (ecHT). The ecHT method maintains the same computational complexity class of the original Hilbert transform allowing implementation in simple digital hardware. We then used the ecHT method to show that the aberrant neural oscillation that hallmarks treatment-resistant essential tremor (ET), the most common adult movement disorder, can be noninvasively supressed via transcranial electrical stimulation at a fixed phase lag over the cerebellar hemisphere ipsilateral to the tremor movement. This was tested in a quadruple-replication randomized way including stimulation at 6 fixed phases, sham and without phase-locking. In a total of 11 subjects, the suppression of ET activity was sustained after the end of the stimulation and was phenomenologically predicted, post-hoc, from the features of the tremor movement before the start of the stimulation. To test for reproducibility, 6 of the original participants werer stimulated exactly the same way three years after the original experiments: the observed, significant stimulation response remained, i.e. responders continued to respond and non-responders did not. Finally, we used a highly-comparative feature extraction (> 8000 features) with statistical learning and neurophysiological computational modelling to show that the suppression of ET activity can be mechanistically attributed to a disruption of the temporal coherence in the tremor movement that can be originated in a higher bursting entropy at the cortico-olivo-cerebello-thalamic circuitry. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodul
Data augmentation is an effective method to increase the quantity of training data, which improves the model's robustness and generalization ability. In this paper, we propose a generative adversarial network (GAN...
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ISBN:
(纸本)9781538656280;9781538656273
Data augmentation is an effective method to increase the quantity of training data, which improves the model's robustness and generalization ability. In this paper, we propose a generative adversarial network (GAN) based data augmentation approach for probabilistic linear discriminant analysis (PLDA), which is a standard back-end for state-of-the-art x-vector based speaker verification system. Instead of generating new spectral feature samples, a conditional Wasserstein GAN is adopted to directly generate x-vectors. Experiments are carried out on the standard NIST SRE 2016 evaluation dataset. Compared to manually adding noise, the GAN augmented PLDA achieves better performance and this performance can be further boosted when combined with manual augmented data. EER of 11.68% and 4.43% were obtained for Tagalog and Cantonese evaluation condition, respectively.
Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number o...
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Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].
We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under ...
We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.
The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connec...
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Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domai...
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ISBN:
(纸本)9781538646595
Policy optimization is the core part of statistical dialogue management. Deep reinforcement learning has been successfully used for dialogue policy optimization for a static pre-defined domain. However, when the domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be then used as a database search constraint is added, or the policy for one domain is transferred to another domain, the dialogue state space and action sets both will change. Therefore, the model structures for different domains have to be different. This makes dialogue policy adaptation/transfer challenging. Here a multi -agent dialogue policy (MADP) is proposed to tackle these problems. MADP consists of some slot-dependent agents (S-Agents) and a slot-independent agent (G-Agent). S-Agents have shared parameters in addition to private parameters for each one. During policy transfer, the shared parameters in S-Agents and the parameters in G-Agent can be directly transferred to the agents in extended/new domain. Simulation experiments showed that MADP can significantly speed up the policy learning and facilitate policy adaptation.
作者：
Ren, LiliangXie, KaigeChen, LuYu, KaiKey Lab. of Shanghai Education
Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China
Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches, it's difficult to scale to ...
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The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships betwee...
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The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships between the complex structural connectome, critical state, and functional diversity remain unknown. Herein, we extend the eigenmode analysis to investigate the joint contribution of hierarchical modular structural organization and critical state to brain functional diversity. We show that the structural modes inherent to the hierarchical modular structural connectome allow a nested functional segregation and integration across multiple spatiotemporal scales. The real brain hierarchical modular organization provides large structural capacity for diverse functional interactions, which are generated by sequentially activating and recruiting the hierarchical connectome modes, and the critical state can best explore the capacity to maximize the functional diversity. Our results reveal structural and dynamical mechanisms that jointly support a balanced segregated and integrated brain processing with diverse functional interactions, and they also shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders.
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