Metaverse: The Re-Confirmation of Human Subjectivity

The metaverse is a major product of the entry of human beings into digital civilization. Under the spirit of Report of the 20th National Congress of the Communist Party of China, the wave of information revolution and the new model of “metaverse+”, it is a major issue to promote Chinese path to modernization and create a new form of human civilization, and it is important to discuss the re-confirmation of human subjectivity in the metaverse, and it is important to answer a series of questions such as what is human and what is the role of human subjectivity in the metaverse. This paper takes the environment and characteristics of human beings as the starting point to answer the question of human subjectivity, and analyzes the confirmation of freedom and consciousness in choosing living space, the confirmation of autonomy in switching cognitive perspectives, the confirmation of dynamism in expanding social interactions, and the confirmation of creativity in empowering digital technology in the metaverse, and deeply examines the relationship between human beings and the metaverse

Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees

Many problems, such as online ad display, can be formulated as online bipartite matching. The crucial challenge lies in the nature of sequentially-revealed online item information, based on which we make irreversible matching decisions at each step. While numerous expert online algorithms have been proposed with bounded worst-case competitive ratios, they may not offer satisfactory performance in average cases. On the other hand, reinforcement learning (RL) has been applied to improve the average performance, but it lacks robustness and can perform arbitrarily poorly. In this paper, we propose a novel RL-based approach to edge-weighted online bipartite matching with robustness guarantees (LOMAR), achieving both good average-case and worst-case performance. The key novelty of LOMAR is a new online switching operation which, based on a judicious condition to hedge against future uncertainties, decides whether to follow the expert's decision or the RL decision for each online item. We prove that for any $\rho\in[0,1]$, LOMAR is $\rho$-competitive against any given expert online algorithm. To improve the average performance, we train the RL policy by explicitly considering the online switching operation. Finally, we run empirical experiments to demonstrate the advantages of LOMAR compared to existing baselines. Our code is available at: https://github.com/Ren-Research/LOMARComment: Accepted by ICML 202

A Personalized Commodities Recommendation Procedure and Algorithm Based on Association Rule Mining

The double-quick growth of EB has caused commodities overload, where our customers are not longer able to efficiently choose the products adapt to them. In order to overcome the situation that both companies and customers are facing, we present a personalized recommendation, although several recommendation systems which may have some disadvantages have been developed. In this paper, we focus on the association rule mining by EFFICIENT algorithm which can simple discovery rapidly the all association rules without any information loss. The EFFICIENT algorithm which comes of the conventional Aprior algorithm integrates the notions of fast algorithm and predigested algorithm to find the interesting association rules in a given transaction data sets. We believe that the procedure should be accepted, and experiment with real-life databases show that the proposed algorithm is efficient one

Effects of vibrational excitation on the F + H2O → HF + OH reaction: dissociative photodetachment of overtone-excited [F-H-OH].

The reaction F + H2O → HF + OH is a four-atom system that provides an important benchmark for reaction dynamics. Hydrogen atom transfer at the transition state for this reaction is expected to exhibit a strong dependence on reactant vibrational excitation. In the present study, the vibrational effects are examined by photodetachment of vibrationally excited F-(H2O) precursor anions using photoelectron-photofragment coincidence (PPC) spectroscopy and compared with full six-dimensional quantum dynamical calculations on ab initio potential energy surfaces. Prior to photodetachment at hνUV = 4.80 eV, the overtone of the ionic hydrogen bond mode in the precursor F-(H2O), 2νIHB at 2885 cm-1, was excited using a tunable IR laser. Experiment and theory show that vibrational energy in the anion can be effectively carried away by the photoelectron upon a Franck-Condon photodetachment, and also show evidence for an increase of branching into the F + H2O reactant channel. The experimental results suggest a greater role for product rotational excitation than theory. Improved potential energy surfaces and longer wavepacket propagation times would be helpful to further examine the nature of the discrepancy