Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings

Genuinely Distributed Byzantine Machine Learning

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a Byzantine behavior. Several approaches have been proposed recently to tolerate Byzantine workers. Yet all require trusting a central parameter server. We initiate in this paper the study of the ``general'' Byzantine-resilient distributed machine learning problem where no individual component is trusted. We show that this problem can be solved in an asynchronous system, despite the presence of $\frac{1}{3}$ Byzantine parameter servers and $\frac{1}{3}$ Byzantine workers (which is optimal). We present a new algorithm, ByzSGD, which solves the general Byzantine-resilient distributed machine learning problem by relying on three major schemes. The first, Scatter/Gather, is a communication scheme whose goal is to bound the maximum drift among models on correct servers. The second, Distributed Median Contraction (DMC), leverages the geometric properties of the median in high dimensional spaces to bring parameters within the correct servers back close to each other, ensuring learning convergence. The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers. MDA requires loose bound on the variance of non-Byzantine gradient estimates, compared to existing alternatives (e.g., Krum). Interestingly, ByzSGD ensures Byzantine resilience without adding communication rounds (on a normal path), compared to vanilla non-Byzantine alternatives. ByzSGD requires, however, a larger number of messages which, we show, can be reduced if we assume synchrony.Comment: This is a merge of arXiv:1905.03853 and arXiv:1911.07537; arXiv:1911.07537 will be retracte

Breakpoints and deleted genes identification of ring chromosome 18 in a Chinese girl by whole-genome low-coverage sequencing: a case report study

The serological examination results. (DOCX 13 kb

Rereading the Classics: Cognition and Practice of Goodness

The issue of the good and the evil has always been the moral cultivation discussed by ancient-modern and Chinese-foreign scholars, whose works are the wisdom treasures and classics. In the 21st century with the emergence of various problems such as the environmental deterioration, global warming, regional conflicts, epidemics and other uncertainty, it is of immeasurable significance to rereading many works for the reflection and interpretation of goodness implication so that their excellent traditional culture on goodness can continue to nourish and enlighten today’s moral education, and awaken a moral consciousness to have habitual memory and cognition of goodness, to stick to good will and do good deeds to commit to improving people’s well-being. It’s more important for global people through interpretations in cultural inheritance to set up a universal concept and standard on goodness to standardize human thoughts and behavior, which are certain to contribute to solution to the above problems and the construction of social harmony and a community with a shared future for mankind

New Thoughts on College Teachers’ Training

With the continuous improvement and development of teaching quality in colleges, college teachers’ training has achieved fast development and been strengthened significantly. However, conditions are various at different times and problems are complicated. Concerning the issue of college teachers’ training, the authors put forward several new thoughts, i.e., enriching the training contents, enhancing the diversity of training methods, improving teachers’ initiatives and enthusiasm, and adopting modern training tools

Research on Personalized Recommender System for Tourism Information Service

Since the development in the 1990s, Recommender system has been widely applied in various fields. The conflict between the expansion of tourism information and difficulty of tourists obtaining tourism information allows Tourism Information Recommender System to have a practical significance. Based on the existing online tourism information service and the mature recommendation algorithms, Personal Recommender System can be used to solve present problems of the key recommendation algorithms. In the first place, this research presents an overview of researches on this issue both at home and abroad, and analyzes the applications of main stream recommendation algorithms. Secondly, a comparative study of domestic and international tourism information service websites is conducted. Drawbacks in their applications are defined and advantages are adopted in the settings of Recommender System. Finally, this research provides the framework of Recommender System, which combines the design and test of algorithms and the existing tourism information recommendation websites. This system allows customers to broaden experience of tourism information service and make tourism decisions more accurately and rapidly. Keywords: Tourism information service, Personalized recommendation, Intelligence recommendation module, Apriori algorith