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