11,359 research outputs found
A Finite Exact Representation of Register Automata Configurations
A register automaton is a finite automaton with finitely many registers
ranging from an infinite alphabet. Since the valuations of registers are
infinite, there are infinitely many configurations. We describe a technique to
classify infinite register automata configurations into finitely many exact
representative configurations. Using the finitary representation, we give an
algorithm solving the reachability problem for register automata. We moreover
define a computation tree logic for register automata and solve its model
checking problem.Comment: In Proceedings INFINITY 2013, arXiv:1402.661
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation for the training data. To address this problem, we propose a novel
deep learning model of Cross-Domain Representation Disentangler (CDRD). By
observing fully annotated source-domain data and unlabeled target-domain data
of interest, our model bridges the information across data domains and
transfers the attribute information accordingly. Thus, cross-domain joint
feature disentanglement and adaptation can be jointly performed. In the
experiments, we provide qualitative results to verify our disentanglement
capability. Moreover, we further confirm that our model can be applied for
solving classification tasks of unsupervised domain adaptation, and performs
favorably against state-of-the-art image disentanglement and translation
methods.Comment: CVPR 2018 Spotligh
Explaining the entrepreneurial intentions of employees: the roles of societal norms, work-related creativity and personal resources.
This article addresses the important question of why those in paid employment might be hesitant to start their own businesses. In particular, we predict how diminished work-related creativity of employees might mediate the relationship between their perceptions that societal norms do not support initiative taking and their own entrepreneurial intentions. In addition, we consider how risk tolerance and passion for work might buffer this process. Survey data, collected among public-sector employees in the United Arabic Emirates, confirm these predictions with the exception of indications for a buffering role of passion for work. For entrepreneurship stakeholders, this research reveals a critical factor – a diminished propensity to generate new ideas at work – by which employee beliefs about limited normative support for enterprising efforts may escalate into a reluctance to consider an entrepreneurial career. It also identifies how this process can be muted when employees are willing to take risks
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