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.

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.

Neural Correlates of Developmental Coordination Disorder: The Mirror Neuron System Hypothesis

Primary impairments of developmental coordination disorder (DCD) include impairments in motor skill, motor learning, and imitation. Such difficulties present challenges for individuals with DCD and may persist into adulthood, negatively impacting daily life in school, work, and social domains. A better understanding of the neural correlates of motor and imitation impairments in DCD holds the potential for informing development of treatment approaches to address these impairments. Although the disorder is assumed to be of neurological origin, little is known of the brain-based etiology of DCD. In recent years the discovery of a fronto-parietal circuit—known as the mirror neuron system—has enabled researchers to better understand imitation, general motor functions, and aspects of social cognition. Given its involvement in imitation and other motor functions, we propose that dysfunction in the mirror neuron system may underlie the characteristic impairments of DCD. We review literature pertaining to the mirror neuron system and develop a theory of disordered mirror neuron functioning in DCD. Finally, we review the limited neuroimaging literature available on neural correlates of DCD and show that the findings from those investigations are congruent with a mirror neuron system theory of DCD. Future research in this population should be designed to investigate specifically mirror neuron regions in individuals with DCD during skilled motor tasks and imitation in particular.

Development and Investigation of the Program Model of Multichannel Queueing System with Unreliable Recoverable Servers in Matlab Environment

In real life, in different industries, we often deal with systems designed for multiple use for performing single-type tasks. Processes taking place at this time are called service of requirements, and the systems themselves—Queueing Systems. This article is dedicated to computer software modelling of processes taking place in the systems in question, Markov processes in particular. In this article, by means of Matlab environment, software realization of one of the typical models of queueing service theory-multichannel QS with unreliable recoverable servers and limited number of requirements in the system, is fulfilled. The results of this research are important because it gives the possibility to use received results to determine optimality degree of some real queueing systems that possess Markov property.

Simulation and Implementation of Real-Time Vision-Based Control System for 2-DoF Robotic Arm Using PID with Hardware-in-the-Loop

Microsoft Kinect sensor has shown the research community that it's more than just an interactive gaming device, due to its multi-functional abilities and high reliability. In this work, online HIL (Hardware-in-the-Loop) experimental data are used to apply human motion imitation to a 2-degree of freedom Lego Mind storm NXT robotic arm. A model simulation of the dc motor used in this experiment is also present in this paper. The acquired input data from the Kinect sensor are processed in a closed loop PID controller with feedback from motors encoders. The applied algorithms solve the overlapping input problem, conducting a simultaneous control of both shoulder and elbow joints, and solving the overlapping input problem as well. The work in this paper is presented as a prototype to assure the applicability of the algorithms, for further development.

Imitative Writing and Variation of“Alice’s Adventures in Wonderland”in Modern China

In 1922,“Alice’s Adventures in Wonderland”was translated and introduced by Zhao Yuanren to China.In the 1930s and 1940s,a series of Chinese-style“Alice”stories appeared.Writers borrowed from the plot structure and character images of“Alice’s Adventures in Wonderland”,making it a work rich in Chinese national language and culture.This article uses the documentary method and comparative research to study the Chinese-style“Alice”stories,and believes that these stories consciously convey new literary concepts,shape children in the new era,and become a part of modern Chinese children’s literature.

A 3D Spectroscopic Vision-based Control Technique for Bilateral Systems

Visual servoing is an active and popular area of research among roboticists.Eventhough visual servoing techniques enhance the perfomance,the associated systems still use traditional methods for their input control.Many research activities and applications have been carried out to implement effective and precise controlling of bilateral systems.This paper presents a 3D spresctroscope-based control technique for bilateral systems.The effectiveness of the available master side designs are evaluated against gesture-based techniques.Joystick control,Electromyography(EMG),Voice control,Haptic control,Exoskeleton control,Gesture and Brain Control Interface(BCI)are identified in the litreature as available bilateral inputs.In the present technnique,Leap Motion Controller(LMC)has been introduced(LMC)to extract the human hand gestures and their parameters.Then these parameters are convereted into respective joint sapce angles using the presented mathematical model.The mathematical models for fingertip mapping,inverse kinematics,dynamics and trajectory generation are implemented and studied.Wolfman Mathematica 10 and MATLAB simulation framework are used to validate the mathematical models,simulations and developed control algorithms.The developed system has sucesfully imitated the fingertip motion.In particular,the system has been able to imitate the figretip motion with a deviation of 6.7%in X axis,5.5%in Y axis and7.9%in Z axis with respect to the expected position.

The Use of Imitation Models at Developing and Introducing Information-Control Systems

Imitation models for computing the environmental water pollution level depending on the intensity of pollution sources created by the author over the years are presented. For this purpose, an additive model of a non-stationary random process is considered. For the modeling of its components, models that consider only dilution and self-purification processes are proposed for waste water and three-dimensional turbulent diffusion equations for river waters, and multidimensional Gaussian Markov series are proposed for modeling the random component. The purpose, the capabilities and the peculiarities of such imitation models are discussed taking into account the peculiarities of the water objects. The modular principle of creating imitation models is proposed to facilitate their development and use.

Exploration and Mapping of Metalliferous Black Shales in the Saghro Temporary Subsidence Zones (Anti-Atlas, Morocco)

The Saghro massif constitutes a vast metallogenic province with numerous deposits and shows of base metals (lead, zinc, copper) and precious metals (gold and silver), besides various useful substances (talc, pyrophyllite, barite, fluorite). Silver/lead occurrences are concentrated along the Cryogenian Imiter series and moderately at Boumalne and Sidi Flah. Copper occupies the plutonic intrusions and intrusive rocks of the East-Central Saghro while barite deposits are widespread throughout the Cambrian cover of the East Saghro in contact with the Ediacaran basement. To justify this distribution, the new contributions of the cartography and the organic geochemistry of the black shales of Jbel Saghro have clearly shown the particularity of the Imiter black shales in terms of the richness in organic matter (TOC = 0.18%), the blackish color and the friability. The Boumalne and Sidi Flah groups present some similarities with the Imiter group, such as the sub-equatorial structuring, the friable pelites and the richness in organic matter (Boumalne TOC = 0.11% and SidiFlah TOC = 0.16%), which is a quite good show that requires to reinforce the exploration works. For Western Saghro in the Iknioun and Qalaa’t M’Gouna groups, the variations in the thickness of the volcanic cover show an irregular paleotopography with hard, greenish, organic-poor pelitic sediments (TOC = 0.01 to 0.04%). We can conclude that the formation of Imiter-type silver concentrations requires the combination of the sedimentological, the volcanic and structural factors. For Imiter-type silver these factors are: a fine pelitic and argillic casing deposited in a confined environment, a basic volcanism source of metals and other intermediate to acid generated by the hydrothermalism and heat, a convenable paleotopography and a network of fracturations to trap the mineralizations.

College English Listening and Speaking Teaching Guided by Memetics

Memetics confirms the importance of recitation and imitation in college English teaching.Starting with the explanation of the replication cycle of language memes,this paper discusses how to use the memetic perspective in college English listening and speaking teaching,effectively transform the input language information into language output,and improve the classroom effect of college English listening and speaking teaching.

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...

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。

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.

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。

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.

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.