54 research outputs found
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators
This paper presents a novel framework to realize proprioception and
closed-loop control for soft manipulators. Deformations with large elongation
and large bending can be precisely predicted using geometry-based sensor
signals obtained from the inductive springs and the inertial measurement units
(IMUs) with the help of machine learning techniques. Multiple geometric signals
are fused into robust pose estimations, and a data-efficient training process
is achieved after applying the strategy of sim-to-real transfer. As a result,
we can achieve proprioception that is robust to the variation of external
loading and has an average error of 0.7% across the workspace on a
pneumatic-driven soft manipulator. The realized proprioception on soft
manipulator is then contributed to building a sensor-space based algorithm for
closed-loop control. A gradient descent solver is developed to drive the
end-effector to achieve the required poses by iteratively computing a sequence
of reference sensor signals. A conventional controller is employed in the inner
loop of our algorithm to update actuators (i.e., the pressures in chambers) for
approaching a reference signal in the sensor-space. The systematic function of
closed-loop control has been demonstrated in tasks like path following and
pick-and-place under different external loads
Changes in Cortical Thickness in Patients With Early Parkinson’s Disease at Different Hoehn and Yahr Stages
Objectives: This study was designed to explore changes in cortical thickness in patients with early Parkinson’s disease (PD) at different Hoehn and Yahr (H-Y) stages and to demonstrate the association of abnormally altered brain regions with part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).Materials and Methods: Sixty early PD patients and 29 age- and gender-matched healthy controls (HCs) were enrolled in this study. All PD patients underwent comprehensive clinical and neuropsychological evaluations and 3.0 T magnetic resonance scanning. Patients with H-Y stage ≤1.5 were included in the mild group, and all other patients were included in the moderate group. FreeSurfer software was used to calculate cortical thickness. We assessed the relationship between UPDRS-III and regional changes in cortical thinning, including the bilateral fusiform and the temporal lobe.Results: The average cortical thickness of the temporal pole, fusiform gyrus, insula of the left hemisphere and fusiform gyrus, isthmus cingulate cortex, inferior temporal gyrus, middle temporal cortex and posterior cingulate cortex of the right hemisphere exhibited significant decreasing trends in HCs group and PD groups (i.e., the mild group and moderate group). After controlling for the effects of age, gender, and disease duration, the UPDRS-III scores in patients with early PD were correlated with the cortical thickness of the left and right fusiform gyrus and the left temporal pole (p < 0.05).Conclusion: The average cortical thickness of specific brain regions reduced with increasing disease severity in early PD patients at different H-Y stages, and the UPDRS-III scores of early PD patients were correlated with cortical thickness of the bilateral fusiform gyrus and the left temporal pole
Aspect of Clusters Correlation at Light Nuclei Excited State
The correlation of was probed via measuring the transverse
momentum and width of one , for the first time,
which represents the spatial and dynamical essentialities of the initial
coupling state in Be nucleus. The weighted interaction vertex of
3 reflected by the magnitudes of their relative momentums and relative
emission angles proves the isosceles triangle configuration for 3 at
the high excited energy analogous Hoyle states.Comment: 8 pages, 9 figure
Variation of Tensor Force due to Nuclear Medium Effect
The enhancement of =3(0) state with isospin excited
by the tensor force in the free Li nucleus has been observed, for the
first time, relative to a shrinkable excitation in the Li cluster
component inside its host nucleus. Comparatively, the excitation of
=0(1) state with isospin for these two Li
formations take on an approximately equal excitation strength. The mechanism of
such tensor force effect was proposed due to the intensive nuclear medium role
on isospin =0 state.Comment: 6 pages, 4 figure
Multi-alpha Boson Gas state in Fusion Evaporation Reaction and Three-body Force
The experimental evidence for the Boson gas state in the
C+CMg fusion evaporation reaction is
presented. By measuring the emission spectrum with multiplicity 2 and
3, we provide insight into the existence of a three-body force among
particles. The observed spectrum exhibited distinct tails corresponding to
particles emitted in pairs and triplets consistent well with the
model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*,
indicating the formation of clusters with three-body force in the
Boson gas state.Comment: 7 pages, 6 figure
Influence of Sustainable Environment Based on a SWOT-PEST Model on Sports Tourism Service Integration Development
The rapid growth of the social economy allows the masses to have more free time to enjoy a material and civilized life, and also allows many families to participate in sports tourism activities. However, as the development of sports tourism has just started, it still has some deficiencies in service integration. Meanwhile, the concept of sustainable development has been applied to sports tourism services. Through the survey on service attitudes and quality of the service staff by selected tourists, it was found that, according to the concept of sustainable development, 140 tourists have described service attitude and quality as very good and good, and only 35 tourists made other choices. In addition, to solve the shortcomings of sports tourism, this paper studied sports tourism services according to the sustainable environment of a SWOT-PEST model. Through expert scoring, the SWOT-PEST model was scored and compared with the model previously used. The score after use was above 4.3 points, and the score before use was below 4 points. Therefore, the research methods in this paper were valuable for research into sports tourism services
Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas
Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively. However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity. The multimodal approach has shown effective application prospects in solving these problems. In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs). Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network. The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone. Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively. Therefore, the fusing features have the potential to predict lung metastasis for STSs
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