Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.Comment: 15 pages, 7 figure

Large electric-field induced strain in BiFeO3 ceramics

Large bipolar strain of up to 0.36% (peak-to-peak value) was measured in BiFeO3 ceramics at low frequency (0.1 Hz) and large amplitude (140 kV/cm) of the driving field. This strain is comparable to that achievable in highly efficient Pb-based perovskite ceramics, such as Pb(Zr,Ti)O3 and Pb(Mg,Nb)O3-PbTiO3. The strain showed a strong dependence on the field frequency and is likely largely associated with domain switching involving predominantly non-180{\deg} domain walls. In addition, rearrangement of charged defects by applying electric field of low frequency depins these domain walls, resulting in a more efficient switching and, consequently, an increased response

Ectopic adrenal gland tissue in the left ovary of an elderly woman: a case report

Ectopic adrenal gland in the ovary is very rare case, and even more rarer in older women. We reported a case of ectopic adrenal tissue as an incidental finding in left ovary from a 68-year-old woman. She presented with bearing down sensation due to uterine prolapse for 5 years. Upon physical examination, uterine prolapse grade III, cystocele, and rectocele were observed. Ultrasonography findings showed 0.69 cm intramural myoma, and no specific findings were found in the bilateral adnexae. She underwent a total laparoscopic hysterectomy, bilateral salpingo-oophorectomy, and anterior-posterior repair. The final pathologic diagnosis of the case was ectopic adrenal gland tissue in the left ovary and uterine leiomyoma. No eventful reactions were observed during hospitalization and after discharge. Although ectopic adrenal gland rarely occurs in elderly women and in the pelvic ovaries, it has a risk of neoplastic transformation and accompanying germ cell tumor and sex cord tumor. Hence, if the ectopic adrenal gland tissue is suspected during surgery, the tissue should be removed. Additionally, by closely examining the contralateral ovary, determining whether other lesions are suspected is necessary. If the other lesions including germ cell tumor or sex cord tumor are suspected, a biopsy of the contralateral ovarian tissue should be performed. Thus, gynecologists must have knowledge about ectopic adrenal gland tissues

Adaptive Transient Fault Model for Sensor Attack Detection

This paper considers the problem of sensor attack detection for multiple operating mode systems, building upon an existing attack detection method that uses a transient fault model with fixed parameters. For a multiple operating mode system, the existing method would have to use the most conservative model parameters to preserve the soundness in attack detection, thus not being effective in attack detection for some operating modes. To address this problem, we propose an adaptive transient fault model to use the appropriate parameter values in accordance with the change of the operating mode of the system. The benefit of our proposed system is demonstrated using real measurement data obtained from an unmanned ground vehicle

Immunization Dynamics on a 2-layer Network Model

We introduce a 2-layer network model for the study of the immunization dynamics in epidemics. Spreading of an epidemic is modeled as an excitatory process in a small-world network (body layer) while immunization by prevention for the disease as a dynamic process in a scale-free network (head layer). It is shown that prevention indeed turns periodic rages of an epidemic into small fluctuation. The study also reveals that, in a certain situation, prevention actually plays an adverse role and helps the disease survive. We argue that the presence of two different characteristic time scales contributes to the immunization dynamics observed.Comment: 5 pages, 7 figure