Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study introduces a deep network representation model that seamlessly integrates the text information and structure of a network. The model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. The representation learned with a paragraph vector model is merged with that learned with the VAE to obtain the network representation, which preserves both structure and text information. Comprehensive experiments is conducted on benchmark datasets and find that the introduced model performs better than state-of-the-art techniques.

Transcranial magnetic stimulation (TMS) over the cerebellum facilitates the spinal reflex in healthy humans. The aim of this study was to investigate whether such cerebellar spinal facilitation (CSpF) appears in patients with spinocerebellar ataxia (SCA) presenting with atrophy in the cerebellar gray matter and dentate nucleus. One patient with SCA type 6 and another with SCA type 31 participated in this study. TMS over the right primary motor cortex was used to induce motor-evoked potentials in the right first dorsal interosseous muscle, which were detected using electromyography. Conditioning TMS using interstimulus intervals of 1–8 ms was performed over the right cerebellum as a test to measure cerebellar brain inhibition (CBI). To assess the H-reflex and the M-wave recruitment curve of the right soleus muscle, we performed electrical stimulation of the right tibial nerve. The stimulation intensity was set to that at the center of the H-reflex curve of the ascending limb. To measure CSpF, we delivered TMS over the right cerebellum 100, 110, 120, and 130 ms before the right tibial nerve stimulation. Voxel-based morphometry was used to verify the presence of atrophy in the cerebellar gray matter and dentate nucleus. CBI was absent in both cases. However, a significant facilitation of the H-reflex occurred with an interstimulus interval of 120 ms in both cases. These findings indicate that the pathways associated with the induction of CSpF and CBI are different, and that the cerebellar gray matter and dentate nucleus are not needed for the induction of CSpF. The possible origin of CSpF may be examined by stimulation of other cerebellar deep nuclei or the brainstem.

The present study proposes a new stress evaluation technique using the photoplethysmogram (PTG). Heart rate variability (HRV) is often used to evaluate mental stress. HRV can be measured from an electrocardiogram (ECG). The frequency variability of HRV and mental stress response are related. PTG also indicates changes in emotional response. PTG can easily be measured without body surface electrodes. This method is less invasive than ECG measurement. We attempt herein to evaluate mental stress by wavelet analysis of the PTG. PTG was measured in the resting and mental stress states, and wavelet transformed PTGs were compared. In nine out of ten subjects, the wavelet result for PTG revealed a decrease in the frequency band.

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Wavelet analysis of the finger-tip photo-plethysmogram (PTG) was performed in order to quantify the stress stage. From the result of the wavelet transform of the PTG, we proposed stress index, with which quantification of the mental stress level was easy. In order to confirm the validity of this index, we compared the stress indexes between the stress and resting stages. The relationship between the stress index and the heart rate variability (HRV) was also investigated. The obtained results indicated that using the wavelet transform in analysis showed a remarkable change in the stress index from the resting stage to the stress stage. This change appears in the tidal wave area of the PTG.