114 research outputs found
A study of factors affecting the performance of expatriates working for multinational companies in China
When multinational companies (MNCs) expand globally, they send their employees to work in foreign subsidiaries as expatriates, and these expatriates potentially provide a competitive edge for their success in a global marketplace. Previous researches have studied the challenges, such as language, culture, family characteristics, and adjustment, faced by expatriates. This research expands on these studies by seeking a deeper understanding of adaptation that enhances expatriates’ adaptive performance in China using both quantitative research and qualitative research. Specifically, this research examines factors that can influence the adaptive performance of expatriates in China, including cultural intelligence, learning flexibility, ethnocentrism, cultural distance, and international work experience.Data were collected in subsidiaries of MNCs in China. Quantitative research is the predominant research method adopted and questionnaires were collected from 224 expatriates who are currently working in subsidiaries of MNCs in China but 175 returned questionnaires were actually suitable for data analysis. The results of the regression analysis demonstrate the positive relationship among expatriates’ adaptation, cultural intelligence, cultural distance and adaptive performance in China; in addition, the negative relationship among expatriates’ ethnocentrism, adaptation and adaptive performance in China; however, there is no relationship between expatriates’ learning flexibility and adaptation, cultural intelligence, cultural distance and adaptive performance in China. Furthermore, ten qualitative interviews were included to play a supplementary role in this study to highlight the positive relationship among expatriates international work experience, adaptation, and adaptive performance in China. The results of this study contribute to the understanding of the factors that influence expatriate adaptive performance in China, and the findings of this study can offer valuable insight for multinational companies in terms of their selection and development of international talents
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps
We consider a challenging theoretical problem in offline reinforcement
learning (RL): obtaining sample-efficiency guarantees with a dataset lacking
sufficient coverage, under only realizability-type assumptions for the function
approximators. While the existing theory has addressed learning under
realizability and under non-exploratory data separately, no work has been able
to address both simultaneously (except for a concurrent work which we compare
in detail). Under an additional gap assumption, we provide guarantees to a
simple pessimistic algorithm based on a version space formed by marginalized
importance sampling, and the guarantee only requires the data to cover the
optimal policy and the function classes to realize the optimal value and
density-ratio functions. While similar gap assumptions have been used in other
areas of RL theory, our work is the first to identify the utility and the novel
mechanism of gap assumptions in offline RL with weak function approximation
A DPCA-based online fault indicator for gear faults using three-direction vibration signals
For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox’s driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
This paper studies regret minimization with randomized value functions in
reinforcement learning. In tabular finite-horizon Markov Decision Processes, we
introduce a clipping variant of one classical Thompson Sampling (TS)-like
algorithm, randomized least-squares value iteration (RLSVI). Our
high-probability worst-case regret bound
improves the previous sharpest worst-case regret bounds for RLSVI and matches
the existing state-of-the-art worst-case TS-based regret bounds.Comment: Updated version, bug fixe
Expatriates in China: Experiential Learning, Cultural Intelligence, and Expatriate Effectiveness
Model-free Representation Learning and Exploration in Low-rank MDPs
The low rank MDP has emerged as an important model for studying
representation learning and exploration in reinforcement learning. With a known
representation, several model-free exploration strategies exist. In contrast,
all algorithms for the unknown representation setting are model-based, thereby
requiring the ability to model the full dynamics. In this work, we present the
first model-free representation learning algorithms for low rank MDPs. The key
algorithmic contribution is a new minimax representation learning objective,
for which we provide variants with differing tradeoffs in their statistical and
computational properties. We interleave this representation learning step with
an exploration strategy to cover the state space in a reward-free manner. The
resulting algorithms are provably sample efficient and can accommodate general
function approximation to scale to complex environments
A DPCA-based online fault indicator for gear faults using three-direction vibration signals
For online monitoring and identifying gear faults, a new fault indicator is proposed based on a multivariate statistical technique, dynamic principal component analysis (DPCA), under variable load conditions. In this method, a tri-axial vibration sensor is used to acquire the 3-direction vibration signals of gear in the gear box because it can pick up more abundant fault information than a single axis sensor does. By monitoring the value of the fault indicator, the running state of the gear (normal condition or faults) can be directly identified according to the set thresholds without using any other fault classification methods. To verify the effectiveness, the proposed method is applied on the QPZZ-II rotating machinery fault simulation rig in which the root crack and the tooth broken faults are introduced into the gearbox’s driving gear. Experimental results show that the fault indicator not only can effectively reveal the health state of the gear, but also is without being influenced by the load fluctuation. And, the accuracy rate of fault diagnosis is over 96 %
The mediating role of cultural intelligence to learning flexibility, cultural difference and expatriate effectiveness
PurposeDrawing on experiential learning theory, this study seeks to understand how the perceived cultural difference in a foreign country and learning flexibility, which enables more integrated experiential learning from international experience, influence expatriates’ cultural intelligence (CQ) and consequently their adjustment and job performance.Design/methodology/approachSurvey data were collected from 169 expatriates in China. Polynomial regression analyses were employed to test curvilinear relationships between cultural difference and CQ and between learning flexibility and CQ. Mediation hypotheses were tested either by the MEDCURVE procedure if a curvilinear relationship was confirmed or by the Haye’s Process procedure if a curvilinear relationship was not confirmed and instead a linear relationship was confirmed.FindingsThe results demonstrated a positive relationship between cultural difference and CQ and an inverted U-shape relationship between learning flexibility and CQ. CQ mediated the relationship between cultural difference and expatriate adjustment and partially mediated the relationship between learning flexibility and expatriate adjustment. CQ positively influenced expatriates’ job performance via expatriate adjustment.Practical implicationsOur findings suggest that companies should not hesitate to send expatriates on assignments to culturally very different countries and focus more attention on the selection of expatriates. The findings of this study suggest firms should choose candidates who are moderate or high in learning flexibility and could engage in integrated learning and specialized learning in a more balanced manner.Originality/valueThis research is the first study that examines the influence of learning flexibility on CQ and expatriate effectiveness. It examines cultural difference through the lens of experiential learning theory and argues that cultural difference constitutes “stimuli” in the experiential learning environment for individual learning in an international context. The results advance our knowledge of the role of experiential learning in developing capable global managers.</jats:sec
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
Polyphone disambiguation aims to capture accurate pronunciation knowledge
from natural text sequences for reliable Text-to-speech (TTS) systems. However,
previous approaches require substantial annotated training data and additional
efforts from language experts, making it difficult to extend high-quality
neural TTS systems to out-of-domain daily conversations and countless languages
worldwide. This paper tackles the polyphone disambiguation problem from a
concise and novel perspective: we propose Dict-TTS, a semantic-aware generative
text-to-speech model with an online website dictionary (the existing prior
information in the natural language). Specifically, we design a
semantics-to-pronunciation attention (S2PA) module to match the semantic
patterns between the input text sequence and the prior semantics in the
dictionary and obtain the corresponding pronunciations; The S2PA module can be
easily trained with the end-to-end TTS model without any annotated phoneme
labels. Experimental results in three languages show that our model outperforms
several strong baseline models in terms of pronunciation accuracy and improves
the prosody modeling of TTS systems. Further extensive analyses demonstrate
that each design in Dict-TTS is effective. The code is available at
\url{https://github.com/Zain-Jiang/Dict-TTS}.Comment: Accepted by NeurIPS 202
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