Development of Sichuan Brocade with Imitating Embroidery Effect Based on Free-Floats Interlacing Weave

Owing to the diversity of consumer’s demand, the traditional Sichuan brocade products have to be innovated in order to survive in a competitive environment. Since the construction of traditional Sichuan brocade was composed of basic-weave, the surface of the fabric shows a regular interweaving planar texture and is difficult to represent a three-dimensional effect. Inspired by embroidery handcraft, this paper attempts to achieve the embroidery-like effect on the fabric through the jacquard process. Based on the multi-backed structure of traditional Sichuan brocade, we adopted the zoned-combination design mode and added extra free-floats interlacing weave in the area where we want to show the embroidered effect and arranged the interlacing points by referring to the feature of the pattern. As a result, designed Sichuan brocades by this method are capable of displaying embroidered effect with high realism and three-dimensionality. This approach improves the artistic effect of the traditional Sichuan brocade and provides a technical reference for further texture design of jacquard fabrics.

A Study on Imitating Activities ,ofHanshan Poems by Chan Buddhist Monks in SONG Dynasty

Although we have no clear picture of the life of Hanshan, a legendary TANG monk and in Collected Poems of Hanshan （Hanshan Sho＇i）, we can find either unclear ideas regarding his major thoughts or different ideologies from Confucianism, Buddhism, and Daoism. Hanshan poetry was broadly read by people belonging to various social statuses during the SONG Dynasty. His poetry was also frequently cited in Chan Buddhist literature of the period. Furthermore, SONG Chan Buddhist monks invited Hanshan into their own genealogy and regarded him as a ＂San Sheng＂ （a Free Sage）. Many Chan Buddhist monks of the SONG Dynasty used Hanshan poetry in various Chan Buddhist texts. Numerous Chan Buddhist monks even wrote so-called ＂ni Hanshan shi＂, which imitated Hanshan poetry as a kind of personal literary creation. It is understandable that when a monk imitated Hanshan poetry, he would simultaneously be both the reader and the creator of Hanshan poetry, and as we understand that every writer produces their works through their own cultural outlook, a newly-formed correlation occurred naturally between the original poetry and imitated poetry through the SONG Chan Buddhist monk＇s version. By observing this correlation, this paper will deeply analyze the dissemination and acceptance of Hanshan poetry, within Chan Buddhist society in the SONG Dynasty, as based on Chan Buddhist literature, in order to learn more about image creation and the recreation of Hanshan during the period

Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

The Study of “Imitation” Aesthetics in Ancient Painting Theory-Taking the Chinese and Western Sources as an Example

The theory of“imitation”in painting occupies a leading position in western art,which originated from the theory of“imitation”in ancient Greece,and has become one of the art theories affecting the world through the continuous development of later generations.Through the exploration of the source of“imitation”in China and the West,there are some comments on the meaning of“imitation”in Chinese classical painting theory,such as“transfer model writing”and“image form”,which is obvious differences from the west.Traditional Chinese painting is a combination of careful observation of natural things and subjective emotions to express their own aesthetic feelings,and ultimately form a vivid artistic conception.Modern imitation is borrowed from Western imitation.In fact,imitation in traditional painting has its own meaning,which contains Chinese aesthetic thought.“Imitation”aesthetics is unique in traditional Chinese painting and is the most important form of painting art.

桑树品种多性状综合评估研究

桑树品种多性状综合评估研究孙日彦，宋慧贞，梁明芝，樊孔彰，杜建勋，王兆红（山东省蚕业研究所，烟台２６４００２）鉴定桑树新品种有许多项目，每一桑品种在这些项目上的表现也互有高低，而且这些项目的重要性及项目之间的关系各不相同。迄今对桑树品种的审定，主要是...

桑树多倍体资源及育种研究概况

桑树多倍体资源及育种研究概况孙日彦，梁明芝，宋慧贞，王林，樊孔彰，杜建勋，王兆红（山东省蚕业研究所，烟台２６４００２）（山东临沂丝绸公司，临沂２７６００３）桑树多倍体尤其是三倍体，一般具有较多的有经济价值的特征、特性。已广泛引起育种工作者的重视。自８...

Math Imitation of the Texture of Particle Cluster Emitted from Diesel Engine

Aim The particle texture from diesel engine was imitated by use of computer. Methods The theory of fractal geometry and the diffusion limited aggregation model were used to simulate the micron texture. Results The fractal dimensions of granule distribution and corpuscle superficial area are quite conformed with those of measurement. Conclusion The texture parameters of engine particle cluster can be obtained precisely by use of fractal theory.

新仿牛黄的抗惊厥作用

牛黄为脊椎动物哺乳纲牛科牛BostaursdomesticusGmelin的胆结石。由于天然牛黄极为短缺,价格昂贵,因而许多学者都在进行人工牛黄的研究。五十年代,唐德煊首先进行了人工合成牛黄的研究并获得成功。后经朱颜进行的药理实验表明,人工牛黄对古柯碱和咖啡碱引起的小鼠惊厥有对抗作用,此点与天然牛黄的药理作用是一致的。1971年,卫生部组织了人工牛黄药理实验协作小组进行了实验,观察到人工牛黄对戊四氮引起的小鼠惊厥也有较好的对抗作用。同年年底。

汉译英仿写式教学在翻译课堂中的应用

本文分析了传统翻译教学,尤其是实用翻译的汉译英教学存在的问题,提出了运用互联网作为教学辅助手段在当代翻译理论和工作坊理念的支撑下,运用仿写来提高学生综合翻译能力。

Some Massively Parallel Algorithms from Nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes.

Influences of Different Habitats on Asexual Propagation of Wild Gastrodia elata f. glauca being Domesticated in Ganzi

[Objective] The influences of different habitats on asexual propagation of wild Geg(abbreviation for Gastrodia elata Bl. f. glauca S. Chow) being domesticated in Ganzi prefecture was studied. [Method] The research trial was carried out in the following three kinds of habitats at the same time: the plastic greenhouses, the birch forest of shady slope and the shrubbery of sunny slope, and the results were analyzed with group data average hypothesis test method. [Result] The overall tuber(the juvenile tuber, the immature tuber and the mature tuber) yield and the mature tuber yield were both very significantly higher in the plastic greenhouses than in the other two types of habitats, and respectively reached 9.52 and 6.70 kg/m2; the mature tuber drying rate was dramatically or very dramatically lower in the plastic greenhouses than in the other two types of habitats, and was 23.84%; the stabilities of the overall tuber yield, the mature tuber yield and the drying rate were all reduced in the order of greenhouses, birch forest and shrubbery; and the dry mature tuber yield of the plastic greenhouses was the highest in the three kinds of habitats, and was 1.60 kg/m2. [Conclusion] A good habitat had to be created or chosen in the domesticating cultivation of the wild Geg in Ganzi prefecture. The preferred choice was the plastic greenhouse, the alternative one was the deciduous broadleaved forest of shady slope. The imitating wild planting of the wild Geg should be done in the warm moist deciduous broad-leaved forest of shady slope.

UG in Imitation

UG and imitation are two parallel hypotheses trying to answer how childrens language acquisition is realized. Imitation fails to explain how children acquire language; however, it helps a lot in childrens language acquisition.

Structural Analysis of Interactive Multimedia-Internet-based Teaching System in College English Teaching

It is of vital importance for modern college English teaching to properly construct an interactive multimedia-internet-based teaching system, the structure of which is clearly elaborated in this paper. An IMITS usually consists of hardware, software, teaching and management. At the end of this paper, a conclusion is made that only when all the four parts of IMITS are construct ed such as is demonstrated, can the IMITS exert its full effects in college English teaching.

IMT-Advanced研究和标准化进展

文章介绍了ITU-RWP8F、欧盟WINNER项目、FuTURE计划和未来移动通信论坛几个IMT-Advanced研究组织的标准化进展,展望了未来宽带无线移动通信技术的发展趋势。

Design and Implementation of a 3D Ocean Virtual Reality and Visualization Engine

In this study,a 3D virtual reality and visualization engine for rendering the ocean,named VV-Ocean,is designed for marine applications.The design goals of VV-Ocean aim at high fidelity simulation of ocean environment,visualization of massive and multidimensional marine data,and imitation of marine lives.VV-Ocean is composed of five modules,i.e.memory management module,resources management module,scene management module,rendering process management module and interaction management module.There are three core functions in VV-Ocean:reconstructing vivid virtual ocean scenes,visualizing real data dynamically in real time,imitating and simulating marine lives intuitively.Based on VV-Ocean,we establish a sea-land integration platform which can reproduce drifting and diffusion processes of oil spilling from sea bottom to surface.Environment factors such as ocean current and wind field have been considered in this simulation.On this platform oil spilling process can be abstracted as movements of abundant oil particles.The result shows that oil particles blend with water well and the platform meets the requirement for real-time and interactive rendering.VV-Ocean can be widely used in ocean applications such as demonstrating marine operations,facilitating maritime communications,developing ocean games,reducing marine hazards,forecasting the weather over oceans,serving marine tourism,and so on.Finally,further technological improvements of VV-Ocean are discussed.

Evolutionary game dynamics of combining the Moran and imitation processes

One of the assumptions of previous research in evolutionary game dynamics is that individuals use only one rule to update their strategy. In reality, an individual's strategy update rules may change with the environment, and it is possible for an individual to use two or more rules to update their strategy. We consider the case where an individual updates strategies based on the Moran and imitation processes, and establish mixed stochastic evolutionary game dynamics by combining both processes. Our aim is to study how individuals change strategies based on two update rules and how this affects evolutionary game dynamics. We obtain an analytic expression and properties of the fixation probability and fixation times(the unconditional fixation time or conditional average fixation time) associated with our proposed process. We find unexpected results. The fixation probability within the proposed model is independent of the probabilities that the individual adopts the imitation rule update strategy. This implies that the fixation probability within the proposed model is equal to that from the Moran and imitation processes. The one-third rule holds in the proposed mixed model. However, under weak selection, the fixation times are different from those of the Moran and imitation processes because it is connected with the probability that individuals adopt an imitation update rule. Numerical examples are presented to illustrate the relationships between fixation times and the probability that an individual adopts the imitation update rule, as well as between fixation times and selection intensity. From the simulated analysis, we find that the fixation time for a mixed process is greater than that of the Moran process, but is less than that of the imitation process. Moreover, the fixation times for a cooperator in the proposed process increase as the probability of adopting an imitation update increases; however, the relationship becomes more complex than a linear relationship.

NOMA-Based Energy-Efficient Task Scheduling in Vehicular Edge Computing Networks: A Self-Imitation Learning-Based Approach

Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.

The “Wrong Love” Between the Zone of Proximal Development and Scaffolding:An Interview With Prof.James P.Lantolf

The zone of proximal development(ZPD) and the scaffolding theory are very different,both in terms of their theoretical origins and connotations,and can even be said to be very different.However,during the development of the two concepts,some scholars have misunderstood them,resulting in the two being mistaken for similar concepts and therefore often confused.Professor James Lantolf from Pennsylvania State University(State College,USA) was interviewed by Professor Lili Qin from Dalian University of Foreign Studies(Dalian,China) and provides an indepth analysis of these issues.The interview begins with the theoretical roots,connotations and definitions of the ZPD and scaffolding concepts,and then unravels the story of how they have been“mistakenly loved for life”,and ultimately it is made clear that the two concepts are completely different in their practical application to language teaching and should not continue to be used interchangeably.

Target-driven visual navigation in indoor scenes using reinforcement learning and imitation learning

Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions are mainly three folds:first,a frame-work combining imitation learning with deep reinforcement learning is presented,which enables a robot to learn a stable navigation policy faster in the target-driven navigation task.Second,the surrounding images is taken as the observation instead of sequential images,which can improve the navigation performance for more information.Moreover,a simple yet efficient template matching method is adopted to determine the stop action,making the system more practical.Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches,which demonstrate the effectiveness and efficiency of our approach.

Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks

Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.