Chinese decision-making in reponse to foreign policy crises, 1949-1996 : a poliheuristic analysis

Title from PDF of title page (University of Missouri--Columbia, viewed on April 21, 2014).China is seen widely as a distinctive power when dealing with international relations in general and foreign policy crises in particular. Given the concerns about whether the rise of China will be peaceful or belligerent, this dissertation aims to illuminate how Chinese decision-makers make key decisions in foreign policy crises and what lead to such decisions in a systematic and theoretically driven way. To achieve this goal, this project tests the Poliheuristic Theory (PH), developed by Alex Mintz (1993, 2003a), which synthesizes the previously isolated psychological and rational theories of foreign policy decision-making. The evidence from the structured, focused comparative analysis of the processes and outcomes of Chinese decision-making in foreign policy crises, spanning from 1949 to 1996, clearly supports the core of PH in such a least-likely context. In a state as distinctive as China, crisis decision-making in the leadership of the Chinese Communist Party (CCP) is not significantly deviant from that in many other states; Chinese decision-makers also make policies against domestic politics. In non-democratic systems, foreign policy decision-makers do not necessarily seek for re-election; however, they tend to seek for legitimacy and public support. Chinese decision-makers put primacy on political survivability at the onset of crisis decision-making. Their political survivability is closely associated with intra-CCP factional struggles, public legitimacy, and individual personalities. Following the initial elimination of politically unacceptable options, Chinese decision-makers do appear to switch to the compensatory rule of utility-maximizing to optimize the final choice with a comprehensive evaluation of the remaining options across all policy dimensions concerning national security

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss. To overcome this issue, we introduce the reconfigurable intelligent surface (RIS) into the CF mMIMO system as a low-cost and power-efficient solution. In this paper, we focus on optimizing the joint precoding design of the RIS-aided CF mMIMO system to maximize the sum SE. This involves optimizing the precoding matrix at the APs and the reflection coefficients at the RIS. To tackle this problem, we propose a fully distributed multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy logic (FL). Unlike conventional approaches that rely on alternating optimization techniques, our FL-based MARL algorithm only requires local channel state information, which reduces the need for high backhaul capacity. Simulation results demonstrate that our proposed FL-MARL algorithm effectively reduces computational complexity while achieving similar performance as conventional MARL methods

Geochemical process and water quality evaluation of karst groundwater in the Baiquan Spring area under exploitation of coal and iron resources and inflow of southern water into Hebei Province

Under the new water situation of the south water into Hebei, the groundwater environment of Baiquan Spring area has been changed, and the geochemical process of karst groundwater needs to be clarified.In this research, numerical simulation, machine learning (self-organized clustering) and isotope (δD and δ18O) methods were used to systematically reveal the geochemical processes of karst groundwater in the Baiquan Spring area under the mining activities and the inflow of water from the south into Hebei Province, and the water quality was evaluated on the basis of entropy-variable weighted water quality index(EWQI). After the entry of south water into Hebei, the karst groundwater level in the Baiquan spring area was raised as a whole. Temporally, it presented both inter-annual dynamic changes and intra-annual seasonal changes. Spatially, the recharge area displayed a drastic change type, while the runoff and discharge areas showed a slow change type.The groundwater dropping funnel was mainly distributed in the coal and iron ore intensive area in the southeast of the spring area. The karst groundwater in the spring area was weakly alkaline, and the hydrochemical types were dominated by Ca−HCO3 and Ca−SO4 types. The major anion and cation concentrations followed the order of ρ(\begin{document}${\mathrm{HCO}}_3^{-}$\end{document}) > ρ(\begin{document}${\mathrm{SO}}_4^{2-}$\end{document}) > ρ(Cl−) and ρ(Ca2+) > ρ(Mg2+) > ρ(Na+) > ρ(K+). The ions showed a gradually increasing distribution pattern along the runoff path.Karst groundwater chemistry was dominated by weathering dissolution of rocks (calcite, dolomite and gypsum) and reverse cation exchange.The contents of \begin{document}${\mathrm{SO}}_4^{2-}$\end{document} and \begin{document}${\mathrm{NO}}_3^{-}$\end{document} in karst groundwater system in spring were affected by anthropogenic activities to some extent.Karst groundwater was derived from atmospheric precipitation and underwent secondary evaporation prior to infiltration, with the deuterium surplus value being reduced during runoff.The results of the water quality evaluation showed that the quality of karst groundwater was overall better than quaternary groundwater. Approximately 50% of the karst water and 37.5% of the quaternary water samples were suitable for drinking purposes, respectively. TDS, ρ(\begin{document}${\mathrm{SO}}_4^{2-}$\end{document}) and ρ(\begin{document}${\mathrm{NO}}_3^{-}$\end{document}) are crucial indicators affecting groundwater quality in the spring area.Potential anthropogenic activities causing water quality degradation in the karst groundwater system of the springs mainly include mine drainage, agricultural irrigation, and urban sewage discharge.Groundwater environmental protection measures for the spring area were proposed through water quality grading evaluation.The research results will be helpful to provide reference for the safety of water supply and the protection of groundwater environment in Baiquanspring area

Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions

Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of low computational complexity and fast convergence rate, there exist several challenges including partial observability, non-stationary, and scalability. In this article, we investigate a novel MARL with graph neural network-aided communication (GNNComm-MARL) to address the aforementioned challenges by making use of graph attention networks to effectively sample neighborhoods and selectively aggregate messages. Furthermore, we thoroughly study the architecture of GNNComm-MARL and present a systematic design solution. We then present the typical applications of GNNComm-MARL from two aspects: resource allocation and mobility management. The results obtained unveil that GNNComm-MARL can achieve better performance with lower communication overhead compared to conventional communication schemes. Finally, several important research directions regarding GNNComm-MARL are presented to facilitate further investigation

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

Aligning Large Language Models from Self-Reference AI Feedback with one General Principle

In aligning large language models (LLMs), utilizing feedback from existing advanced AI rather than humans is an important method to scale supervisory signals. However, it is highly challenging for AI to understand human intentions and societal values, and provide accurate preference feedback based on these. Current AI feedback methods rely on powerful LLMs, carefully designed specific principles to describe human intentions, and are easily influenced by position bias. To address these issues, we propose a self-reference-based AI feedback framework that enables a 13B Llama2-Chat to provide high-quality feedback under simple and general principles such as ``best for humanity``. Specifically, we allow the AI to first respond to the user's instructions, then generate criticism of other answers based on its own response as a reference, and finally determine which answer better fits human preferences according to the criticism. Additionally, we use a self-consistency method to further reduce the impact of position bias, and employ semantic perplexity to calculate the preference strength differences between different answers. Experimental results show that our method enables 13B and 70B Llama2-Chat annotators to provide high-quality preference feedback, and the policy models trained based on these preference data achieve significant advantages in benchmark datasets through reinforcement learning.Comment: 19 pages, 3 figure

Prediction model for severe autoimmune encephalitis: a tool for risk assessment and individualized treatment guidance

BackgroundSevere autoimmune encephalitis (AE) can cause significant neurological deficits, status epilepticus, status dystonicus, and even death, which can be life-threatening to patients. Accurate risk stratification for severe AE progression is critical for optimizing therapeutic strategies. The comprehensive prediction models for severe AE based on routine clinical data and laboratory indicators remain lacking.ObjectiveTo develop and validate a prediction model for severe AE to optimize individualized treatment.MethodsWe collected clinical data and laboratory examination results from 207 patients with confirmed AE. The study population was divided into development and validation cohort. A prediction model for severe AE was constructed using a nomogram and was rigorously validated both internally and externally. Severe AE was defined as modified Rankin Scale (mRS) > 2 and Clinical Assessment Scale for Encephalitis (CASE) > 4.ResultsThe variables ultimately included in the nomogram for the severe AE predictive model were age, psychiatric and/or behavioral abnormalities, seizures, decreased level of consciousness, cognitive impairment, involuntary movements, autonomic dysfunction, and increased intrathecal IgG synthesis rate. It demonstrated excellent discriminative capacity and calibration through internal-external validation.ConclusionThe prediction model has highly feasibility in clinical practice, and holds promise as an important tool for risk assessment and guiding individualized treatment in patients with AE

A rapid identification model of mine water inrush based on PSO-XGBoost

Mine water inrush is one of the main threats to mine safety production. Rapid analysis of the cause of water inrush and accurate identification of water inrush source are the key steps of mine water inrush disaster control. In order to effectively prevent and control mine water inrush disaster and identify mine water inrush source accurately and quickly, a mine water inrush source identification model (PSO-XGBoost) based on particle swarm optimization algorithm (PSO) and limit gradient lifting regression tree (XGBoost) was proposed. The efficiency and accuracy of water inrush source identification were further improved by the efficient parameter global search model, and the model was successfully applied to the Laohutai mine in Fushun coal field, Liaoning Province to verify the practicability of the model. Based on the spectral data of 40 groups of water samples from Laohutai mine, the original spectral data were preprocessed by multiple scattering correction, smoothing denoising, standardization and principal component analysis, and the training set and test set were divided according to the ratio of 7∶3 according to stratified random sampling. Secondly, the individual optimal value and the global optimal value of particles are initialized, and PSO is used to iteratively optimize seven parameters of XGBoost algorithm, such as learning_rate, n_estimatiors, max_depth, etc., to construct the classification and recognition model under the optimal parameter combination. To further investigate the superiority of the model, the average discrimination accuracy and log loss value were selected as evaluation indexes to compare the classification recognition results of PSO-XGBoost model with PSO-SVM and PSO-RF models, while the generalization ability of each model was evaluated by 100 repetitions of cross-validation. The comparison results showed that the average discrimination accuracies of XGBoost, PSO-SVM, PSO-RF and PSO-XGBoost models for the test set data were 87.76%, 87.56%, 91.67% and 91.67%, respectively. For repeated cross-validation, the average accuracy of XGBoost, PSO-SVM, PSO-RF, and PSO-XGBoost models were 87.76%, 87.56%, 90.63%, and 93.18%, respectively, with corresponding log-loss averages of 0.5453, 0.5460, 0.5623, and 0.4534, respectively. Comprehensive analysis of evaluation indexes shows that PSO-XGBoost model has higher discrimination accuracy and better generalization ability in mine water inrush source identification

MetaRM: Shifted Distributions Alignment via Meta-Learning

The success of Reinforcement Learning from Human Feedback (RLHF) in language model alignment is critically dependent on the capability of the reward model (RM). However, as the training process progresses, the output distribution of the policy model shifts, leading to the RM's reduced ability to distinguish between responses. This issue is further compounded when the RM, trained on a specific data distribution, struggles to generalize to examples outside of that distribution. These two issues can be united as a challenge posed by the shifted distribution of the environment. To surmount this challenge, we introduce MetaRM, a method leveraging meta-learning to align the RM with the shifted environment distribution. MetaRM is designed to train the RM by minimizing data loss, particularly for data that can improve the differentiation ability to examples of the shifted target distribution. Extensive experiments demonstrate that MetaRM significantly improves the RM's distinguishing ability in iterative RLHF optimization, and also provides the capacity to identify subtle differences in out-of-distribution samples.Comment: 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2401.0608

Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3

The stacked intelligent metasurface (SIM) emerges as an innovative technology with the ability to directly manipulate electromagnetic (EM) wave signals, drawing parallels to the operational principles of artificial neural networks (ANN). Leveraging its structure for direct EM signal processing alongside its low-power consumption, SIM holds promise for enhancing system performance within wireless communication systems. In this paper, we focus on SIM-assisted multi-user multi-input and single-output (MU-MISO) system downlink scenarios in the transmitter. We proposed a joint optimization method for SIM phase shift configuration and antenna power allocation based on the twin delayed deep deterministic policy gradient (TD3) algorithm to efficiently improve the sum rate. The results show that the proposed algorithm outperforms both deep deterministic policy gradient (DDPG) and alternating optimization (AO) algorithms. Furthermore, increasing the number of meta-atoms per layer of the SIM is always beneficial. However, continuously increasing the number of layers of SIM does not lead to sustained performance improvement.Comment: accepted by IEEE GLOBECOM 202