Score-Based Equilibrium Learning in Multi-Player Finite Games with Imperfect Information

Real-world games, which concern imperfect information, multiple players, and simultaneous moves, are less frequently discussed in the existing literature of game theory. While reinforcement learning (RL) provides a general framework to extend the game theoretical algorithms, the assumptions that guarantee their convergence towards Nash equilibria may no longer hold in real-world games. Starting from the definition of the Nash distribution, we construct a continuous-time dynamic named imperfect-information exponential-decay score-based learning (IESL) to find approximate Nash equilibria in games with the above-mentioned features. Theoretical analysis demonstrates that IESL yields equilibrium-approaching policies in imperfect information simultaneous games with the basic assumption of concavity. Experimental results show that IESL manages to find approximate Nash equilibria in four canonical poker scenarios and significantly outperforms three other representative algorithms in 3-player Leduc poker, manifesting its equilibrium-finding ability even in practical sequential games. Furthermore, related to the concept of game hypomonotonicity, a trade-off between the convergence of the IESL dynamic and the ultimate NashConv of the convergent policies is observed from the perspectives of both theory and experiment

Survey of Automatic Labeling Methods for Topic Models

Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development

Phase transition and structure of silver azide at high pressure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98719/1/JApplPhys_110_023524.pd

A \u3cem\u3eLIN28B\u3c/em\u3e Tumor-Specific Transcript in Cancer

The diversity and complexity of the cancer transcriptome may contain transcripts unique to the tumor environment. Here, we report a LIN28B variant, LIN28B-TST, which is specifically expressed in hepatocellular carcinoma (HCC) and many other cancer types. Expression of LIN28B-TST is associated with significantly poor prognosis in HCC patients. LIN28B-TST initiates from a de novo alternative transcription initiation site that harbors a strong promoter regulated by NFYA but not c-Myc. Demethylation of the LIN28B-TST promoter might be a prerequisite for its transcription and transcriptional regulation. LIN28B-TST encodes a protein isoform with additional N-terminal amino acids and is critical for cancer cell proliferation and tumorigenesis. Our findings reveal a mechanism of LIN28B activation in cancer and the potential utility of LIN28B-TST for clinical purposes

Picturing Electron Capture to the Continuum in the Transfer Ionization of Intermediate-Energy He²⁺ Collisions with Argon

Electron emission occurring in transfer ionization for He2+ collisions with argon has been investigated using cold target recoil ion momentum spectroscopy. The double differential cross sections for electron capture to the continuum of the projectile (cusp-shaped electrons) are presented for collision energies from 17.5 to 75 keV/u. For an energy of 30 keV/u, we find a maximum in the experimental ratio of the cusp-shaped electron yield to the total electron yield. This result is explained in terms of the velocity matching between the projectile ion and the electron initially bound to the target. One of the important issues for double electron transitions is the role of electron-electron correlation. If this correlation is weak, then the transfer-ionization process can be viewed as two separate sequential processes. If this correlation is strong, then the transfer-ionization process would happen simultaneously and not sequentially. Our experimental and theoretical results indicate that correlation is weak and that the first step is target ionization followed by charge capture

Neural and fuzzy dynamic programming for under-actuated systems

This paper aims to integrate the fuzzy control with adaptive dynamic programming (ADP) scheme, to provide an optimized fuzzy control performance, together with faster convergence of ADP for the help of the fuzzy prior knowledge. ADP usually consists of two neural networks, one is the Actor as the controller, the other is the Critic as the performance evaluator. A fuzzy controller applied in many fields can be used instead as the Actor to speed up the learning convergence, because of its simplicity and prior information on fuzzy membership and rules. The parameters of the fuzzy rules are learned by ADP scheme to approach optimal control performance. The feature of fuzzy controller makes the system steady and robust to system states and uncertainties. Simulations on under-actuated systems, a cart-pole plant and a pendubot plant, are implemented. It is verified that the proposed scheme is capable of balancing under-actuated systems and has a wider control zone. © 2012 IEEE