Memorandum on China’s Measures for Addressing Sea Level Change

This paper describes the current state of China’s recognition of sea level rise in the context of global climate change. The author analyzes official state documents addressing sea level rise, including the annual China Sea Level Communiqué, and compares them with local government initiatives and perspectives from non-governmental sources such as academia, NGOs and the general public. The paper concludes that, while China has taken many commendable steps towards addressing sea level rise, there are still considerable obstacles to be overcome. Finally, the author recommends that local governmental and non-governmental actors play a larger and better defined role. The author also calls for better criticism of and greater transparency in current government efforts as well as more robust institutions for enforcement, assessment and public participation

A Few Good Men: A Quantitative Analysis of High-Level People\u27s Liberation Army (PLA) Promotion Patterns under Xi Jinping

China’s “striving for achievement,” or fenfa youwei (奋发有为) foreign policy strategy challenges U.S. regional primacy, emphasizes Chinese leadership in foreign affairs, and vigorously promotes economic and strategic initiatives favorable to China. According to State Department analyst Elizabeth Hague, People’s Liberation Army (PLA) promotion patterns will most likely change in response to China’s new economic and strategic demands. However, there is currently little analysis on exactly how PLA promotion patterns are changing. This thesis fills the gap by statistically analyzing how age, personal connections, education, professional experience, and foreign experience are associated with the grade promotions, not rank promotions, of 275 high-level PLA officers under Xi Jinping, defined as officers at or above the grade of corps leader (正军级). This study allows U.S. policymakers to better understand how the PLA is directing its hard power resources to support the fenfa youwei strategy, track the types of officers who are likely to fill PLA leadership positions in the future, and prepare policy responses to address shifting PLA strategic priorities. This thesis has five major findings. 1) As a high-level officer gets one year older under Xi, his or her odds of promotion decrease by a factor of .804. 2) High-level Xi-era officers who have served in the Lanzhou or Shenyang Military Regions at or above the corps leader grade sometime in their careers are more likely to receive promotions. 3) Each additional level of education (from a middle school education to a doctorate) that a high-level Xi-era officer achieves increases his or her odds of promotion by a factor of 1.413. 4) High-level Xi-era officers with experience serving in two or more PLA services, branches, and danwei (work units), at or above the corps leader grade are 2.639 times more likely to be promoted than officers without such experience. 5) Combat experience during wartime, non-combat experience (including counterterrorism experience, disaster relief experience, and experience leading military ceremonies), and international experience do not significantly increase the likelihood of high-level PLA promotions under Xi. This thesis does not address the change in the PLA’s structure that has occurred since the PLA began its massive reorganization in early 2016. New methodologies will be required to quantitatively analyze PLA promotion patterns after this reorganization

Non-equilibrium Theoretical Framework and Universal Design Principles of Oscillation-Driven Catalysis

At stationary environmental conditions, a catalyst's reaction rates may be restricted by thermodynamic laws, and certain performances can never be achieved (e.g., catalysts can not change the free energy difference between reactants and products). However, it has been reported that if environments change rapidly, catalysts can be driven away from stationary states and exhibit anomalous performance. We present a general geometric non-equilibrium theory to describe and explain anomalous catalytic behaviors in rapidly oscillating environments that exceed the steady-state restrictions. It leads to a universal design principle of novel catalysts with oscillation-pumped performances. Even though a catalyst at various environmental conditions cannot be described by a single free energy landscape, we propose a novel control-conjugate landscape to encode the reaction rates over a continuous range of control parameters $\lambda$, which is inspired by the exponential form of the Arrhenius law. The control-conjugate landscape significantly simplifies the design principle and makes it applicable to large-amplitude environmental oscillations. The design principle is demonstrated by two examples, (1) inverting a spontaneous reaction to synthesize high-free-energy molecules and (2) speeding up reactions without utilizing low activation barriers. In both examples, catalysts autonomously harness energy from non-equilibrium environments to enable such functionalities

Gradient estimates for porous medium and fast diffusion equations by martingale method

International audienceIn this paper, we establish several local and global gradient estimates for the positive solution of Porous Medium Equations (PMEs) and Fast Diffusion Equations (FDEs). Our proof is probabilistic and uses martingale techniques

Volatility forecasting with machine learning and intraday commonality

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs

Sharp Spectral Gap and Li-Yau's Estimate on Alexandrov Spaces

In the previous work [35], the second and third authors established a Bochner type formula on Alexandrov spaces. The purpose of this paper is to give some applications of the Bochner type formula. Firstly, we extend the sharp lower bound estimates of spectral gap, due to Chen-Wang [9, 10] and Bakry-Qian [6], from smooth Riemannian manifolds to Alexandrov spaces. As an application, we get an Obata type theorem for Alexandrov spaces. Secondly, we obtain (sharp) Li-Yau's estimate for positve solutions of heat equations on Alexandrov spaces.Comment: 19 pages, final version, to appear in Math.

Long-tail Cross Modal Hashing

Existing Cross Modal Hashing (CMH) methods are mainly designed for balanced data, while imbalanced data with long-tail distribution is more general in real-world. Several long-tail hashing methods have been proposed but they can not adapt for multi-modal data, due to the complex interplay between labels and individuality and commonality information of multi-modal data. Furthermore, CMH methods mostly mine the commonality of multi-modal data to learn hash codes, which may override tail labels encoded by the individuality of respective modalities. In this paper, we propose LtCMH (Long-tail CMH) to handle imbalanced multi-modal data. LtCMH firstly adopts auto-encoders to mine the individuality and commonality of different modalities by minimizing the dependency between the individuality of respective modalities and by enhancing the commonality of these modalities. Then it dynamically combines the individuality and commonality with direct features extracted from respective modalities to create meta features that enrich the representation of tail labels, and binaries meta features to generate hash codes. LtCMH significantly outperforms state-of-the-art baselines on long-tail datasets and holds a better (or comparable) performance on datasets with balanced labels.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence(AAAI2023

An online review-driven two-stage hotel recommendation model considering customers’ risk attitudes and personalized preferences

Hotel recommendation models provide crucial references for customers to select their ideal hotels and help them overcome information overload. However, previous models primarily focus on capturing public preferences, neglecting personalized preferences or different risk attitudes among customers. To address this gap, this paper proposes a novel two-stage hotel recommendation model driven by online reviews, incorporating customers’ risk attitudes and personalized preferences. Firstly, this paper utilizes the Latent Dirichlet Allocation (LDA) topic extraction model and the sentiment analysis tool to extract public and personalized preferences from hotel reviews and customers’ historical reviews respectively. Secondly, in the first stage of hotel recommendation, this paper constructs a hotel filtering mechanism to cater to customers with different risk attitudes, ensuring that the recommended hotels align with customers’ individual risk tolerance. In the second stage of hotel recommendation, this paper introduces the cosine similarity algorithm of probabilistic linguistic term sets, enabling more accurate and tailored recommendations. Finally, to verify the applicability of the proposed model, a case study is conducted using real data from TripAdvisor.com. The results of the comparative analysis indicate that the proposed model outperforms other recommendation models.<br/