A Stylistic Analysis of Alexander Tcherepnin\u27s Piano Concerto No. 4, Op. 78, With an Emphasis on Eurasian Influences

Russian-born Alexander Nikolayevich Tcherepnin (Jan 23, 1899-Sept 13, 1977), a contemporary of Virgil Thomson, Henry Cowell, Igor Stravinsky, and Arnold Schoenberg, made far-reaching contributions as a pianist, conductor, and composer. His unique contrapuntal system “Interpoint,” employment of rhythmic variation, adoption of polyphonic structure, and use of the nine-step scale secured his place in music history as a celebrated composer of the twentieth century.1 Throughout his life he traveled to various countries. 1928-1947 is a period during which Tcherepnin came under the influence of “Eurasian” ideas (the synthesis of Russian and Eastern cultures). He developed new musical formulas by exploiting musical folklore in eastern countries. The last year of this phase is marked by his Piano Concerto No. 4, Op. 78 (Fantasia for piano and orchestra), composed in Paris in 1947. The Concerto is based on Chinese legend, related in the form of a poem, and influenced by his tour (1934-37) in China where he helped budding careers of young Chinese composers and composed several chamber and solo piano works based on Chinese art. The Concerto was premiered in December 16, 1958, Oakland, California by Lily Bohnki-Roenthal under the direction of Piero Bellugi. In contrast to the works written in China, the Concerto No. 4 involves more than the mere use of Chinese idioms. There is a clear connection with the traditions of Debussy and Ravel, which is reflected in the treatment of the coloristic possibilities. In the concerto, Tcherepnin revived the nine-step scale that he had temporarily abandoned by the late 1920s. The mingling of the Chinese style and his innovative technique, combined with the tradition of Romanticism, Impressionism and the Russian school, is the culmination of the composer’s accomplishment under the influence of Eurasianism. This study examines Tcherepnin’s musical life from the perspectives of a Eurasian artist, a Chinese scholar, and an accomplished musician, and suggests the adoption of a hybrid style in the Concerto No. 4 based on a thorough musical analysis. An investigation of the Concerto No. 4 increases the awareness of the composer and his creative style

An Optimal Method for Diffusion Parameters of Nonlinear Diffusion Problem of Drug Releasing in 2D-Disc Device by Separate Variable Method

An optimization control model and the corresponding computational method drawing the diffusion parameters of the nonlinear problem for the drug releasing in the 2D-disc device were given in this paper. Firstly, based on the nonlinear diffusion equation of the drug releasing in the 2D-disc device, we used the linear diffusion problem to discrete the nonlinear diffusion problem with the discrete space and the discrete time. Then, by the separate variable method, the solution of the linear problem was given. Next, the least square method based on the separate variable idea (LSMSV) was used to estimate the nonlinear appropriate diffusion parameters. Finally, a numerical example was presented to show that the control model and the numerical method were valid for computing the diffusion coefficient of the nonlinear problem for the drug releasing in the 2D-disc device

Retraction: Novel two-stage surgical treatment for Cantrell syndrome complicated by severe pulmonary hypertension: a case report

INTRODUCTION: Cantrell syndrome is a rare syndrome of congenital defects, which can be complicated by severe pulmonary hypertension and left ventricular diverticulum; it has proved difficult to treat in clinical practice. CASE PRESENTATION: A 6-month-old Han Chinese baby girl weighing 3.5kg was diagnosed, using ultrasonography and radiography, as having Cantrell syndrome complicated by severe pulmonary hypertension. For safety, we divided management into two stages. For the first stage, we dealt with the left ventricular diverticulum and pulmonary hypertension. Three months later, we performed diorthosis for an intracardiac malformation. CONCLUSIONS: Cantrell syndrome with pulmonary hypertension may respond well to this novel two-stage operation, which needs more verification via clinical practice

Joint Resource Management and Storage-aware Routing with Heterogeneous Access for Wireless Ad-hoc Networks Routing

In this paper, the design and implementation details of wireless ad-hoc terminals are proposed to build a rapidly deployable, frequency-agile, heterogeneous wireless network. Several key protocol methods have been identified including name/address separation, robustness concerning link quality variation and disconnection, multi-homing, ad-hoc network formation, flexible inter-domain boundaries and resource coordination. This paper investigates the wireless ad-hoc terminals architecture that can be used to build rapidly deployable wireless network application, while the Heterogeneous Routing with Resource Management for Mobility (HRRM) mechanism is identified. The results show that the proposed approach has advantage over other scheduling schemes in optimizing incentives for both accuracy cross channel widths and signal attenuation, leading to highly successful time to discovery and resource management

Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.The Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant Number: NSC 100-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science & Technology in Taiwan (Grant Numbers: CSIST-095-V101 and CSIST-095-V102). Furthermore, it was supported by the National Science Foundation of China (No.50935005)

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

Deep learning models usually suffer from domain shift issues, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. Under this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation tasks: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-center prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.Comment: Preprin

Causality-inspired single-source domain generalization for medical image segmentation

Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains