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Enabling large language models to effectively utilize real-world tools is crucial for achieving embodied intelligence. Existing approaches to tool learning have primarily relied on either extremely large language models, such as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or have utilized supervised learning to train limited types of tools on compact models. However, it remains uncertain whether smaller language models can achieve generalized tool-use abilities without specific tool-specific training. To address this question, this paper introduces ToolAlpaca, a novel framework designed to automatically generate a tool-use corpus and learn generalized tool-use abilities on compact language models with minimal human intervention. Specifically, ToolAlpaca first collects a comprehensive dataset by building a multi-agent simulation environment, which contains 3938 tool-use instances from more than 400 real-world tool APIs spanning 50 distinct categories. Subsequently, the constructed corpus is employed to fine-tune compact language models, resulting in two models, namely ToolAlpaca-7B and ToolAlpaca-13B, respectively. Finally, we evaluate the ability of these models to utilize previously unseen tools without specific training. Experimental results demonstrate that ToolAlpaca achieves effective generalized tool-use capabilities comparable to those of extremely large language models like GPT-3.5. This validation supports the notion that learning generalized tool-use abilities is feasible for compact language models.

Procedural text understanding requires machines to reason about entity states within the dynamical narratives. Current procedural text understanding approaches are commonly \textbf{entity-wise}, which separately track each entity and independently predict different states of each entity. Such an entity-wise paradigm does not consider the interaction between entities and their states. In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner. Based on this paradigm, we propose \textbf{S}cene \textbf{G}raph \textbf{R}easoner (\textbf{SGR}), which introduces a series of dynamically evolving scene graphs to jointly formulate the evolution of entities, states and their associations throughout the narrative. In this way, the deep interactions between all entities and states can be jointly captured and simultaneously derived from scene graphs. Experiments show that SGR not only achieves the new state-of-the-art performance but also significantly accelerates the speed of reasoning., 9 pages, 2 figures

Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. Specifically, we model PLMs as a meta-function $\mathcal{ \lambda_ {\text{instruction, demonstrations, text}}. M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i.e., $\mathcal{ (\lambda . M) }$(instruction, demonstrations) $\to$ $\mathcal{F}$ where $\mathcal{F}$ will be a new entity extractor, i.e., $\mathcal{F}$: text $\to$ entities. To inject the above in-context NER ability into PLMs, we propose a meta-function pre-training algorithm, which pre-trains PLMs by comparing the (instruction, demonstration)-initialized extractor with a surrogate golden extractor. Experimental results on 4 few-shot NER datasets show that our method can effectively inject in-context NER ability into PLMs and significantly outperforms the PLMs+fine-tuning counterparts., Comment: Accepted to ACL 2023 Main Conference

Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark document understanding researches, this paper summarizes four representative abilities, i.e., document classification, document structural analysis, document information extraction, and document transcription. Under the new evaluation framework, we propose \textbf{Document Language Understanding Evaluation} -- \textbf{DLUE}, a new task suite which covers a wide-range of tasks in various forms, domains and document genres. We also systematically evaluate six well-established transformer models on DLUE, and find that due to the lengthy content, complicated underlying structure and dispersed knowledge, document understanding is still far from being solved, and currently there is no neural architecture that dominates all tasks, raising requirements for a universal document understanding architecture.

Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction -- knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains., 14 pages

Knowledge plays a critical role in artificial intelligence. Recently, the extensive success of pre-trained language models (PLMs) has raised significant attention about how knowledge can be acquired, maintained, updated and used by language models. Despite the enormous amount of related studies, there still lacks a unified view of how knowledge circulates within language models throughout the learning, tuning, and application processes, which may prevent us from further understanding the connections between current progress or realizing existing limitations. In this survey, we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods, and investigating how knowledge circulates when it is built, maintained and used. To this end, we systematically review existing studies of each period of the knowledge life cycle, summarize the main challenges and current limitations, and discuss future directions., paperlist: https://github.com/c-box/KnowledgeLifecycle

The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas. In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing. In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand. Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings., Comment: accepted by AAAI2023

Large language models (LLMs) such as ChatGPT and GPT-4 have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point for LLMs. It remains unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are GPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlying commonsense knowledge for answering a specific question? (4) Can GPTs effectively leverage commonsense for answering questions? To evaluate the above commonsense problems, we conduct a series of experiments to evaluate ChatGPT's commonsense abilities, and the experimental results show that: (1) GPTs can achieve good QA accuracy in commonsense tasks, while they still struggle with certain types of knowledge. (2) ChatGPT is knowledgeable, and can accurately generate most of the commonsense knowledge using knowledge prompts. (3) Despite its knowledge, ChatGPT is an inexperienced commonsense problem solver, which cannot precisely identify the needed commonsense knowledge for answering a specific question, i.e., ChatGPT does not precisely know what commonsense knowledge is required to answer a question. The above findings raise the need to investigate better mechanisms for utilizing commonsense knowledge in LLMs, such as instruction following, better commonsense guidance, etc.

Large language models (LLMs) have gained increasing prominence in artificial intelligence, making a profound impact on society and various industries like business and science. However, the presence of false information on the internet and in text corpus poses a significant risk to the reliability and safety of LLMs, underscoring the urgent need to understand the mechanisms of how false information influences the behaviors of LLMs. In this paper, we dive into this problem and investigate how false information spreads in LLMs and affects related responses. Specifically, in our series of experiments, we investigate different factors that can influence the spread of information in LLMs by comparing three degrees of information relevance (direct, indirect, and peripheral), four information source styles (Twitter, web blogs, news reports, and research papers) and two common knowledge injection paradigms (in-context injection and learning-based injection). The experimental results show that (1)False information will spread and contaminate related memories in LLMs via a semantic diffusion process, i.e., false information has global detrimental effects beyond its direct impact. (2)Current LLMs are susceptible to authority bias, i.e., LLMs are more likely to follow false information presented in trustworthy styles such as news reports and research papers, which usually cause deeper and wider pollution of information. (3)Current LLMs are more sensitive to false information through in-context injection than through learning-based injection, which severely challenges the reliability and safety of LLMs even when all training data are trusty and correct. The above findings raise the need for new false information defense algorithms to address the global impact of false information, and new alignment algorithms to unbiasedly lead LLMs to follow essential human values rather than superficial patterns.

Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities. In recent years, large-scale pre-trained language models have shown remarkable memorizing ability. On the contrary, vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting problem. To investigate such a retentive-forgetful contradiction and understand the memory mechanism of language models, we conduct thorough experiments by controlling the target knowledge types, the learning strategies and the learning schedules. We find that: 1) Vanilla language models are forgetful; 2) Pre-training leads to retentive language models; 3) Knowledge relevance and diversification significantly influence the memory formation. These conclusions are useful for understanding the abilities of pre-trained language models and shed light on designing and evaluating new learning and inference algorithms of language models.

This paper proposes a two-stage bi-layer game charging optimization model based on the background of non-coordination between a distribution network operator (DNO), a distributed generation operator (DGO), and a charging agent (CA). In the first stage, a dynamic virtual price-based demand response (DVPBDR) model is constructed to pre-optimize the charging load with the virtual charging cost as the objective. In the second stage, a strategy for adjusting output deviations based on a bi-layer Stackelberg game model is established, with the economic benefits of each participant as the objectives. Full cooperation mode and bi-layer mixed game are introduced to compare with the bi-layer Stackelberg game in Simulation Analysis. The calculation results show that: (1) the DVPBDR is not constrained by the actual electricity price system and mechanism, and reflect the real influence of price changes on charging demands, thus effectively reducing energy abandonment by 41.76% and net load fluctuation by 53.50%; (2) in the full cooperation mode, there is a conflict of interests and CA suffers financial loss, thus resulting in a reduction in comprehensive benefits by at least 61.08%, compared to the non-cooperative cases; (3) in the bi-layer mixed games, the cooperative gain of DGO and DNO is superior than that of the cooperation between DGO and CA, so a relative win-win is achieved in the bi-layer mixed game with DGO-CA cooperation, and the comprehensive benefits is increased by 3.32%; (4) in the bi-layer Stackelberg game, each participant has a completely independent awareness of decision-making and establishes strategies for maximizing its own interests, which results in achieving the optimal comprehensive benefits (increases by at least 44.18% compared to other cases). Therefore, multi-dimensional benefits are realized in the multi-participant charging system with a bi-layer Stackelberg game.

Entity matching (EM) is the most critical step for entity resolution (ER). While current deep learningbased methods achieve very impressive performance on standard EM benchmarks, their realworld application performance is much frustrating. In this paper, we highlight that such the gap between reality and ideality stems from the unreasonable benchmark construction process, which is inconsistent with the nature of entity matching and therefore leads to biased evaluations of current EM approaches. To this end, we build a new EM corpus and re-construct EM benchmarks to challenge critical assumptions implicit in the previous benchmark construction process by step-wisely changing the restricted entities, balanced labels, and single-modal records in previous benchmarks into open entities, imbalanced labels, and multimodal records in an open environment. Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching. The constructed benchmarks and code are publicly released, Accepted to IJCAI2022

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs). Unfortunately, recent studies have discovered such an evaluation may be inaccurate, inconsistent and unreliable. Furthermore, the lack of understanding its inner workings, combined with its wide applicability, has the potential to lead to unforeseen risks for evaluating and applying PLMs in real-world applications. To discover, understand and quantify the risks, this paper investigates the prompt-based probing from a causal view, highlights three critical biases which could induce biased results and conclusions, and proposes to conduct debiasing via causal intervention. This paper provides valuable insights for the design of unbiased datasets, better probing frameworks and more reliable evaluations of pretrained language models. Furthermore, our conclusions also echo that we need to rethink the criteria for identifying better pretrained language models. We openly released the source code and data at https://github.com/c-box/causalEval., Accepted to the main conference of ACL2022

Based on the four wave mixing, a three mode nonlinear system is proposed. The single photon blockade is discussed through analytical analysis and numerical calculation. The analytical analysis shows that the conventional photon blockade and unconventional photon blockade can be realized at the same time, and the analytical conditions of the two kinds of blockade are the same. The numerical results show that the system not only has the maximum average photon number in the blockade region, but also can have strong photon anti-bunching in the region with small nonlinear coupling coefficient, which greatly reduces the experimental difficulty of the system. This optical system which can realize the compound photon blockade effect is helpful to realize the high-purity single photon source.

Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism - structural schema instructor, and captures the common IE abilities via a large-scale pre-trained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE., Accepted to the main conference of ACL2022

Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application. Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem to be with similar target but require totally different underlying abilities. In this paper, we propose Multi-Choice Matching Networks to unify low-shot relation extraction. To fill in the gap between zero-shot and few-shot RE, we propose the triplet-paraphrase meta-training, which leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability. Experimental results on three different low-shot RE tasks show that the proposed method outperforms strong baselines by a large margin, and achieve the best performance on few-shot RE leaderboard., Comment: Accepted to the main conference of ACL2022

Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained language models (PLMs). The pre-training tasks for PLMs are not question-answering or MRC-based tasks, making existing PLMs unable to be directly used for unsupervised MRC. Specifically, MRC aims to spot an accurate answer span from the given document, but PLMs focus on token filling in sentences. In this paper, we propose a new framework for unsupervised MRC. Firstly, we propose to learn to spot answer spans in documents via self-supervised learning, by designing a self-supervision pretext task for MRC - Spotting-MLM. Solving this task requires capturing deep interactions between sentences in documents. Secondly, we apply a simple sentence rewriting strategy in the inference stage to alleviate the expression mismatch between questions and documents. Experiments show that our method achieves a new state-of-the-art performance for unsupervised MRC.

The conventional photon blockade for high-frequency mode is investigated in a two-mode second-order nonlinear system embedded with a two-level atom. By solving the master equation and calculating the zero-delay-time second-order correlation function $g^{(2)}(0)$, we obtain that strong photon antibunching can be achieved in this scheme. In particular, we find that by increasing the linear coupling coefficient of the system, a perfect blockade region will be formed near the zero second-order nonlinear coupling coefficient. Similarly, by increasing the nonlinear coupling coefficient of the system, the perfect blockade zone will appear. And this scheme is not sensitive to the reservoir temperature, both of which make the current system easier to implement experimentally.

Event detection has long been troubled by the \emph{trigger curse}: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on ACE05, MAVEN and KBP17 datasets., Comment: Accepted to the main conference of EMNLP2021

In the recent publications [Phys. Rev. A 92,023838 (2015)], the unconventional photon blockade are studied in a two-mode-second-order-nonlinear system with nonlinear coupling between the low frequency and high frequency modes. In this paper, we study the unconventional photon blockade in a three-mode system with weakly coupled nonlinear cavities via $\chi^{(2)}$ nonlinearity. By solving the master equation in the steady-state limit and calculating the zero-delay time second-order correlation function, we obtain the conditions of strong photon antibunching in the low frequency mode. The numerical result are compared with the analytical results, the results show that they are in complete agreement. By the analysis of numerical solutions, we find that this scheme is not sensitive to the change of decay rates and the reservoir temperature, and the three-mode drives make the system have more adjustable parameters, both of which increases the possibility of experimental implementation.

To realize the vision of “carbon neutrality” in China, the use of clean energy and hydrogen energy storages can be integrated to optimize the structure of power systems and ensure safe, stable, and low-carbon operation of power. However, the current cost of hydrogen production is primarily associated to the high price of electricity, and the construction of an integrated energy system is a promising approach to solve this problem. In this study, a solitary grid energy system that integrates water and hydrogen has been developed, which consists of a hydroelectric power station, hydrogen production equipment, a hydrogen storage device, and a fuel cell. Furthermore, a two-layer programming model is designed. The upper layer is aimed at minimizing the construction cost to improve the hydrogen production capacity, hydrogen storage, fuel cell performance, and other facilities. Meanwhile, the lower layer is aimed at minimizing the decrease in daily operations to optimize the utilization of all the facilities in the integrated energy system under different scenarios. The Asynchronous Advantage Actor-Critic (A3C) reinforcement learning algorithm and Gurobi are used to solve the model. The results of typical scenarios show that the water-hydrogen integrated energy system saves water resources, maintains the stability of electric power, and provides improved economic benefits than those offered by single hydroelectric systems, thereby increasing the overall resource, social, and economic values of the system.

Figure 2. (a) Originalin-situVNIS reflectance spectra expressed in reflectance factor measured by CE-4 during the first two lunar days; (b) the six spectra normalized at 750 nm. Comparisons of continuum-removed measurement spectra of six different sites with the corresponding best-matched LUT spectra obtained usingfourdifferent fitting metrics are shown in (c)~ (h). (c)for N15, (d) for N16, (e) for N17-1, (f) for N17-2, (g) for N18-1, and (h) for N18-2, respectively. Figure 3.Average M3spectra of selected typical units. The locations of selected areas in (a) and (b) are marked inFigure 1bandFigure 1a, respectively. Figure 4. The ternary diagram of PLG-PYX-OLV abundances of (a) the CE4 landing site and selected ROIs within the Von Kármán (VK) crater; (b) the CE4 landing site and selected ROIs outside the VK crater extracted from the CE-4, M3and Kaguya Lunar Multiband Imager (MI) data; (c) the plot of CPX abundance vs OPX abundance of the CE4 landing site and selected ROIs extracted from the CE-4, M3and MI data. Note the abundances are relative.The different results of M3and Kaguya/CE-4 are probably caused by their different spatial resolutions. Figure 6.Principal component analysis results of CE-4 landing site.(b) PC2 vs PC1 and (c) PC4 vs PC3 are distributions of the typical PC values of selected ROIs calculated from the M3spectra in the PC space. Figure S1.The original CMOS spectrum, original SWIR spectrum and the scaled CMOSspectrum of N18-1 measurement site. Reflectance was obtained by using Eq.(S1). After scaling the CMOS spectrum to the SWIR spectrum, the two spectra agree with each other well in the overlapping region and the scaling did not introduce any distortions to the 1mm absorption feature. Figure S2.Reflectance in REFF at six measurement sites using solar irradiance calibration (red dots) and the onboard calibration target method (blue squares). Figure S3.Continuum-removed measurement spectraat six sites using the solar irradiance calibration (red dots) and onboard calibration target method (blue squares). Figure S4.Relative differences of REFF for the six measurements shown inFigure S2. The relative difference was obtained by (eq.(S3) – eq.(S1))/eq.(S3)). Figure S7.Retrieved mineral abundances by using reflectance spectra obtained by using the solar irradiance (Eq. S1)) and onboard calibration target (Eq. (S3)) calibration methods. The absolute differences between the two results are also shown. It can be seen that measurements with shadows in either the lunar surface (N17-1) or calibration target (N17-2) have incurred larger differences. Figure S8. Comparisons of continuum-removed measurement spectra of selected areas (Figure 1a) of M3data and the corresponding best-matched LUT spectra obtained usingfourdifferent fitting metrics (Equations (S4)-(S7))for (a) CE-4 landing site, (b) F1, (c) F2, (d)A1, (e) A2, (f) VKM, (g) VK, (h) H1 and (i) H2, respectively. Figure S9. Comparisons of continuum-removed measurement spectra of selected areas (Figure 1b) of M3data and the corresponding best-matched LUT spectra obtained usingfourdifferent fitting metrics (Equations (S4)-(S7))for (a) CP, (b) C1, (c) C2, (d)C3, (e) C4, (f) C5, (g) C6, (h) C7 and (i) C8, respectively. Figure S10. Cumulative variances of the first ten principal components. &nbsp

20世纪号称“美国世纪”，美国作为当今世界最有影响力的世界大国，其强国之路有一定的历史必然性。这种必然性表现为主客观2个方面：优越的地缘安全环境与难得的时代机遇为美国的发展壮大提供了客观条件；美国的创建者与后来的政治精英们构建了当时世界上最好的国内政治文化与成熟的制度安排，同时又得益于独特的政治思潮与精巧的国际战略谋划，这成为支撑美国大国地位的主观条件。正是得益于这些主客观方面的优势，美国保持了较长时间的繁荣和强盛，而且未来只要这些因素没有发生太大变化，美国作为世界超级大国的地位还将维持相当长的一段时间。 As the leading power in the world, the US has had the most important influence on international society in the 20th century which was named by some as the American Century. The reasons behind the success of the US could be characterized as “4 outstanding”. The first was the outstanding secure environment the US faced when it was emerging. Benefitting from the protection of the Pacific and Atlantic, the US grew to be a world power peacefully and quickly. The second was the outstanding historic opportunity the US experienced when it extended its strength toward the western states. Due to the prevailing attitudes of that time, no European countries criticized the violence and acts of aggression the US committed, which would seem impossible today. The third was the outstanding political culture and institution American founders set up in the 18th and 19th centuries, which helped the US to overcome many strategic challenges, and the fourth was the outstanding international regime designed by US elites, which helped the US to maintain its leading influence on the international community., Journal of China and International Relations, Vol 3 No 2 (2015)

林宏宇 张帅 【摘要】本文尝试对2010年以来中美两国在国际安全领域的大国博弈以及由此形成的中国国家安全环境负面化态势做出一个系统阐释。2010年中国GDP首次超过日本，成为仅次于美国的世界第二大经济体，中国崛起不再是国际关系学者的纸上谈兵，而是确确实实的国际关系现状，这种状况客观上打破了东亚国际安全格局的平衡，对已有的安全结构造成有力冲击。深感“权力转移”之忧的美国试图以重返亚太（又称“再平衡”）战略来缓阻中国崛起的势头。此战略客观上极大刺激了东亚地区某些国家借美抗华的念头，它们试图以制造事端来“引美入亚”，希望美国“主持公道”。美国也愿借助这些国家对华挑衅来增强其再平衡战略的效果。面对某些东亚国家对华核心国家利益的挑战，和平崛起的中国不得不严峻应对，其后果是中国落个“四处树敌”、国家安全环境负面化的态势。但中国不会就此终结和平崛起之路，美国也不会轻易放弃其国际霸权，这就意味着中美此轮安全博弈会持续相当长一段时间，可能出现一段相当长时间的安全困境。然而主客观条件决定中美不会爆发新冷战，更不会爆发世界大战。面对挑战，中国不应采取同美国针锋相对的战略，而应在美国不威胁中国核心国家利益与安全的基础上，对内继续坚持以经济建设为中心，对外继续坚持“韬光养晦”和“不结盟”，努力构建中美新型大国关系，走出安全困境。 This paper attempts to make a systematic explanation of the big game that has unfolded between China and the United States since 2010 in the field of international security and led to a deterioration of China’s national security environment. In 2010, the GDP of China surpassed the GDP of Japan for the first time, and China became the second largest economic entity in the world, surpassed only by the U.S. The rise of China was no longer just empty talk of a few scholars in the field of international relations. On the contrary, the rebalancing of international security relations changed and shook the existing security architecture in East Asia. As a consequence, the U.S. tried to use a pivot/rebalancing strategy to corrode China’s rising momentum which objectively stimulated some of the countries in East Asia to oppose China with the help of the U.S. The U.S. also wanted to strengthen its rebalancing strategy’s effectiveness via these countries’ actions. In the face of the challenges from these countries on China’s core national interests, China had to take the consequences seriously. But China will continue to adhere to the path of its peaceful rise whereas the U.S. will not give up its international hegemony easily as well. This means that the Sino-U.S. game will continue for quite a long period of time as will the security dilemma. However, it will not lead to a new cold war, let alone the outbreak of an extensive world war between China and the U.S. In the face of challenges, China should not take on the same strategy as the U.S. Only if the U.S. does not threaten the core national interests and the security of China, China should focus on economics internally and externally adhering to the "keeping a low profile" and "non-alignment" principles. China should also strive towards building a new type of relationship between China and the U.S. so that the security dilemma can end., Journal of China and International Relations, Vol 2 No 2 (2014)

International monetary power has become a new area of interest since the outbreak of the global financial crisis in 2008. The USA, the EU and China will pursue global monetary power in the future, and the USD, the EURO and the RMB will become the most important world reserve currencies. The pattern of international monetary power will shift from a unilateral-hegemony structure to a triangle-balance structure. This shift has a very strong influence on changes in the current international political system. As the largest emerging economy, China will speed up the globalization of the RMB to meet the challenges of the international monetary system., Journal of China and International Relations, Vol 1 No 2 (2013)

Is there any influence of supply voltage fluctuation on the shunt active power filter? Few papers have discussed this problem. In this paper, based on the instantaneous power balance of shunt active power filter ac-side and dc-side, a small signal model of dc-link voltage control is derived. According to this small signal model, the influence of supply voltage fluctuation on dc-link voltage of shunt active power filter is analyzed in detail. And the conclusion is that the supply voltage fluctuation has influence on dc-link voltage of shunt active power filter. Then a feedforward control method of the supply voltage and the compensation current is proposed to eliminate the influence in this paper. Finally the experimental results also are given to demonstrate the conclusions.