Ultrafast Collective Dynamics in the Charge-Density-Wave Conductor K0.3_{0.3}MoO3_{3}

Low-energy coherent charge-density wave excitations are investigated in blue bronze (K$_{0.3}$MoO$_{3}$) and red bronze (K$_{0.33}$MoO$_{3}$) by femtosecond pump-probe spectroscopy. A linear gapless, acoustic-like dispersion relation is observed for the transverse phasons with a pronounced anisotropy in K$_{0.33}$MoO$_{3}$. The amplitude mode exhibits a weak (optic-like) dispersion relation with a frequency of 1.67 THz at 30 K. Our results show for the first time that the time-resolved optical technique provides momentum resolution of collective excitations in strongly correlated electron systems.Low-energy coherent charge-density wave excitations are investigated in blue bronze (K$_{0.3}$MoO$_{3}$) and red bronze (K$_{0.33}$MoO$_{3}$) by femtosecond pump-probe spectroscopy. A linear gapless, acoustic-like dispersion relation is observed for the transverse phasons with a pronounced anisotropy in K$_{0.33}$MoO$_{3}$. The amplitude mode exhibits a weak (optic-like) dispersion relation with a frequency of 1.67 THz at 30 K. Our results show for the first time that the time-resolved optical technique provides momentum resolution of collective excitations in strongly correlated electron systems.Comment: 10 pages, 4 figure

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward (i) can greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores, and (iii) has also a superior performance when it is incorporated with extrinsic rewards

Granular computing and optimization model-based method for large-scale group decision-making and its application

In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare some alternatives, so they often express evaluation information with incomplete hesitant fuzzy linguistic preference relations. How to obtain suitable large-scale group decision-making results from incomplete preference information is an important and interesting issue to concern about. After analyzing the existing researches, we find that: i) the premise that complete preference relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given by decision makers are greatly possible to be changed during the consistency improving process. In order to solve these issues, this work proposes a novel method based on Granular computing and optimization model for large-scale group decision-making, considering the original consistency of incomplete hesitant fuzzy linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the rationality and advantages of the proposed method: i) semantics are not changed during the consistency improving process, ii) completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally consistent, iv) final large-scale group decision-making result is acquired by fusing complete preference relations with different weights

Towards Visual Saliency Explanations of Face Recognition

Deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in the past years. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition systems. Recent studies have investigated using visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper conceives a new explanation framework for face recognition. It starts by providing a new definition of the saliency-based explanation method, which focuses on the decisions made by the deep FR model. Then, a novel correlation-based RISE algorithm (CorrRISE) is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Besides, two evaluation metrics are designed to measure the performance of general visual saliency explanation methods in face recognition. Consequently, substantial visual and quantitative results have shown that the proposed method consistently outperforms other explainable face recognition approaches