Wei Yuan

Extreme Learning Machine is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches.

Since the 1980s, psychoanalysis and psychotherapy have attracted great interest in Chinese society, and in parallel, the psychotherapist as a character has emerged in Chinese literature. Through four novels by three Chinese authors, namely, Xu Xiaobin 徐小斌, Ke Yunlu 柯云路, and Bi Shumin 毕淑敏, this article studies the evolution of the image of the psychotherapist in contemporary Chinese literature, which reflects the reception of psychoanalysis as well as the development of psychotherapy in China. In Xu Xiaobin’s novel, two psychology students attempt to cure a psychotic patient through “transference therapy” based on a complete misunderstanding of the notion; Ke Yunlu features a “life counselor” who, like typical good counselors in Chinese classical novels, simply gives advice without caring about the patients’ needs and desires; and the two novels by Bi Shumin show a significant change in the writer’s understanding of the profession and therapeutic methods, as well as the current, unregulated situation of psychotherapy in China.

While cochlear implantation has benefitted many patients with single-sided deafness (SSD), there is great variability in cochlear implant (CI) outcomes and binaural performance remains poorer than that of normal-hearing (NH) listeners. Differences in sound quality across ears – temporal fine structure (TFS) information with acoustic hearing versus coarse spectro-temporal envelope information with electric hearing – may limit integration of acoustic and electric patterns. Binaural performance may also be limited by inter-aural mismatch between the acoustic input frequency and the place of stimulation in the cochlea. SSD CI patients must learn to accommodate these differences between acoustic and electric stimulation to maximize binaural performance. It is possible that training may increase and/or accelerate accommodation and further improve binaural performance. In this study, we evaluated lateralization training in NH subjects listening to broad simulations of SSD CI signal processing. A 16-channel vocoder was used to simulate the coarse spectro-temporal cues available with electric hearing; the degree of inter-aural mismatch was varied by adjusting the simulated insertion depth (SID) to be 25 mm (SID25), 22 mm (SID22), and 19 mm (SID19) from the base of the cochlea. Lateralization was measured using headphones and head-related transfer functions (HRTFs). Baseline lateralization was measured for unprocessed speech (UN) delivered to the left ear to simulate SSD and for binaural performance with the acoustic ear combined with the 16-channel vocoders (UN+SID25, UN+SID22, and UN+SID19). After completing baseline measurements, subjects completed 6 lateralization training exercises with the UN+SID22 condition, after which performance was re-measured for all baseline conditions. Post-training performance was significantly better than baseline for all conditions (p<0.05 in all cases), with no significant difference in training benefits among conditions. Given that there was no significant difference between the SSD and the SSD CI conditions before or after training, the results suggest that NH listeners were unable to integrate TFS and coarse spectro-temporal cues across ears for lateralization, and that inter-aural mismatch played a secondary role at best. While lateralization training may benefit SSD CI patients, the training may largely improve spectral analysis with the acoustic ear alone, rather than improve integration of acoustic and electric hearing.