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

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

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

Clinical features and risk factors of sepsis-induced cardiomyopathy in children with sepsis

Objective To investigate the clinical features and risk factors of sepsis-induced cardiomyopathy in children with sepsis， aiming to provide reference for enhancing the diagnosis and treatment levels of clinicians. Methods Clinical data of children with sepsis were retrospectively analyzed. All patients were divided into the sepsis-induced cardiomyopathy group and non-sepsis-induced cardiomyopathy group according to whether sepsis-induced cardiomyopathy occurred.Clinical characteristics and outcomes were compared between two groups. The risk factors of sepsis-induced cardiomyopathy were analyzed. Results Three hundred and ninty-eight children with sepsis were included in this study， and the incidence of sepsis-induced cardiomyopathy was 15.58%（62/398）. The age of children in the sepsis-induced cardiomyopathy group was 49 （18， 108） months， older than 19 （6， 52） months in the non-sepsis-induced cardiomyopathy group. The incidence of septic shock in the sepsis-induced cardiomyopathy group was 83.87%（52/62）， which was higher than 42.56% （143/336） in the non-sepsis-induced cardiomyopathy group. The mortality rate in the sepsis-induced cardiomyopathy group was 29.03% （18/62）， significantly higher than 14.58% （49/336） in the non-sepsis-induced cardiomyopathy group. All differences were statistically significant （all P < 0.05）.Multivariate Logistic regression analysis showed that the influence of age on sepsis-induced cardiomyopathy was statistically significant （OR=1.010， 95%CI 1.003-1.017， P = 0.006）. The higher the lactic acid level， the higher the risk of sepsis-induced cardiomyopathy， with statistical significance （OR=1.163， 95%CI 1.034-1.308， P = 0.012）. The higher the cTnI level， the higher the risk of sepsis-induced cardiomyopathy， with statistical significance （OR=9.929， 95%CI 4.651-21.197， P < 0.001）. Conclusions Compared with children with non-sepsis-induced cardiomyopathy， children with sepsis-induced cardiomyopathy are more prone to septic shock and have higher mortality. Age， lactic acid and cTnI levels are the independent influencing factors for sepsis-induced cardiomyopathy in children

StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. Our dataset APPS+ and StepCoder are available online.Comment: 13 pages, 5 figure

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization

TSC1/2 Signaling Complex Is Essential for Peripheral Naïve CD8+ T Cell Survival and Homeostasis in Mice

The PI3K-Akt-mTOR pathway plays crucial roles in regulating both innate and adaptive immunity. However, the role of TSC1, a critical negative regulator of mTOR, in peripheral T cell homeostasis remains elusive. With T cell-specific Tsc1 conditional knockout (Tsc1 KO) mice, we found that peripheral naïve CD8+ T cells but not CD4+ T cells were severely reduced. Tsc1 KO naïve CD8+ T cells showed profound survival defect in an adoptive transfer model and in culture with either stimulation of IL-7 or IL-15, despite comparable CD122 and CD127 expression between control and KO CD8+ T cells. IL-7 stimulated phosphorylation of Akt(S473) was diminished in Tsc1 KO naïve CD8+T cells due to hyperactive mTOR-mediated feedback suppression on PI3K-AKT signaling. Furthermore, impaired Foxo1/Foxo3a phosphorylation and increased pro-apoptotic Bim expression in Tsc1 KO naïve CD8+T cells were observed upon stimulation of IL-7. Collectively, our study suggests that TSC1 plays an essential role in regulating peripheral naïve CD8+ T cell homeostasis, possible via an mTOR-Akt-FoxO-Bim signaling pathway

Smoke Image Segmentation Algorithm Suitable for Low-Light Scenes

The real-time monitoring and analysis system based on video images has been implemented to detect fire accidents on site. While most segmentation methods can accurately segment smoke areas in bright and clear images, it becomes challenging to obtain high performance due to the low brightness and contrast of low-light smoke images. An image enhancement model cascaded with a semantic segmentation model was proposed to enhance the segmentation effect of low-light smoke images. The modified Cycle-Consistent Generative Adversarial Network (CycleGAN) was used to enhance the low-light images, making smoke features apparent and improving the detection ability of the subsequent segmentation model. The smoke segmentation model was based on Transformers and HRNet, where semantic features at different scales were fused in a dense form. The addition of attention modules of spatial dimension and channel dimension to the feature extraction units established the relationship mappings between pixels and features in the two-dimensional spatial directions, which improved the segmentation ability. Through the Foreground Feature Localization Module (FFLM), the discrimination between foreground and background features was increased, and the ability of the model to distinguish the thinner positions of smoke edges was improved. The enhanced segmentation method achieved a segmentation accuracy of 91.68% on the self-built dataset with synthetic low-light images and an overall detection time of 120.1 ms. This method can successfully meet the fire detection demands in low-light environments at night and lay a foundation for expanding the all-weather application of initial fire detection technology based on image analysis