Views and Attitudes of Intercultural Awareness in Chinese Teaching and Learning in Shanxi Provincial Universities Context

The perceptions of academic staffs and overseas students to the significance of intercultural awareness in Chinese teaching/learning as foreign language were studied to provide suggestions for enhancing their intercultural communication competence in the context of Shanxi Provincial universities. The participants of 273 students and 52 staffs took part in the questionnaire and 25 of them accepted semi-structured interview. The combination of qualitative and quantitative analysis indicated a significant correlation between intercultural experience, length of Chinese teaching/learning and the enthusiasm in target language involved programs, regardless of age or gender. The attitudes to cultural diversity, misunderstanding even conflicts unavoidably existed in these universities influenced target language and culture mastery. This study suggested the promotion of intercultural awareness among staffs as well as students was important to help international students take advantage of opportunities available at campus or beyond to improve their effective intercultural communication. And much more intercultural strategies, including more positive learning environment, appropriated curriculum, further exploration, concentrated on improving target language proficiency and extending cultural experience in Chinese classes should be implemented to motivate students’ intercultural enthusiasm and competence

On-the-fly Race Detection for Programs with Recursive Spawn-Sync Parallelism

Detecting data race is very important for debugging shared-memory parallel programs, because data races result in unintended nondeterministic execution of the program. We propose a dynamic on-the-fly race detection mechanism called Parallel Nondeterminator to check for determinacy races during the parallel execution of a program with recursive spawn-sync parallelism. A modified version of Nested Region Labeling scheme is developed for the concurrency relationship test in the spawn-sync parallel structure. Through the identification of Least Common Ancestor in the spawn tree, the Parallel Nondeterminator only needs to keep two read access records and one write access record for each shared location. The work and critical path in the instrumented codes are analyzed as well as time complexity and space requirements. Let N denote the maximum depth of the recursion in the parallel program. The worst case time increased for each spawn and sync operation is O(N) and the time required to monitor any shared memory location is O(lgN). Moreover, Parallel Nondeterminator is able to execute the race detection code without loss of parallelism of the original program. In summary, the Parallel Non-determinator represents a provably efficient strategy for detecting data races for shared-memory parallel programs.Singapore-MIT Alliance (SMA

Semi-sparsity Priors for Image Structure Analysis and Extraction

Image structure-texture decomposition is a long-standing and fundamental problem in both image processing and computer vision fields. In this paper, we propose a generalized semi-sparse regularization framework for image structural analysis and extraction, which allows us to decouple the underlying image structures from complicated textural backgrounds. Combining with different textural analysis models, such a regularization receives favorable properties differing from many traditional methods. We demonstrate that it is not only capable of preserving image structures without introducing notorious staircase artifacts in polynomial-smoothing surfaces but is also applicable for decomposing image textures with strong oscillatory patterns. Moreover, we also introduce an efficient numerical solution based on an alternating direction method of multipliers (ADMM) algorithm, which gives rise to a simple and maneuverable way for image structure-texture decomposition. The versatility of the proposed method is finally verified by a series of experimental results with the capability of producing comparable or superior image decomposition results against cutting-edge methods.Comment: 18 page

Strong Convergence Theorems for Maximal Monotone Operators with Nonspreading Mappings in a Hilbert Space

We prove the strong convergence theorems for finding a common element of the set of fixed points of a nonspreading mapping T and the solution sets of zero of a maximal monotone mapping and an α-inverse strongly monotone mapping in a Hilbert space. Manaka and Takahashi (2011) proved weak convergence theorems for maximal monotone operators with nonspreading mappings in a Hilbert space; there we introduced new iterative algorithms and got some strong convergence theorems for maximal monotone operators with nonspreading mappings in a Hilbert space

Semi-Sparsity for Smoothing Filters

In this paper, we propose an interesting semi-sparsity smoothing algorithm based on a novel sparsity-inducing optimization framework. This method is derived from the multiple observations, that is, semi-sparsity prior knowledge is more universally applicable, especially in areas where sparsity is not fully admitted, such as polynomial-smoothing surfaces. We illustrate that this semi-sparsity can be identified into a generalized $L_0$-norm minimization in higher-order gradient domains, thereby giving rise to a new "feature-aware" filtering method with a powerful simultaneous-fitting ability in both sparse features (singularities and sharpening edges) and non-sparse regions (polynomial-smoothing surfaces). Notice that a direct solver is always unavailable due to the non-convexity and combinatorial nature of $L_0$-norm minimization. Instead, we solve the model based on an efficient half-quadratic splitting minimization with fast Fourier transforms (FFTs) for acceleration. We finally demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications

Deformation and orientation effects in the driving potential of the dinuclear model

A double-folding method is used to calculate the nuclear and Coulomb interaction between two deformed nuclei with arbitrary orientations. A simplified Skryme-type interaction is adopted. The contributions of nuclear interaction and Coulomb interaction due to the deformation and orientation of the nuclei are evaluated for the driving potential used in the description of heavy-ion fusion reaction. So far there is no satisfactory theory to describe the evolution of the dynamical nuclear deformation and orientations during the heavy-ion fusion process. Our results estimated the magnitude of above effects.Comment: 15 pages, 6 figures, Accepted by Eur. Phys. Jour.

Predicting recurrence and survival in patients with non-metastatic renal-cell carcinoma after nephrectomy: a prospective population-based study with multicenter validation

Background: Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones. Methods: In this prospective analysis and external validation study, the development set included 11 128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell’s C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation. Results: Of the 11 128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7–7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773–0.806] for PFS and 0.793 [95% CI 0.773–0.811] for OS) and in the external validation set (0.773 [0.731–0.816] and 0.723 [0.731–0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all P<0.0001) for PFS and from 0.680 to 0.744 (all P<0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC. Conclusions: Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival