Can Technological Artefacts Be Moral Agents?

In this paper we discuss the hypothesis that, ‘moral agency is distributed over both humans and technological artefacts’, recently proposed by Peter-Paul Verbeek. We present some arguments for thinking that Verbeek is mistaken. We argue that artefacts such as bridges, word processors, or bombs can never be (part of) moral agents. After having discussed some possible responses, as well as a moderate view proposed by Illies and Meijers, we conclude that technological artefacts are neutral tools that are at most bearers of instrumental value

Do Banks Influence the Capital Structure Choices of Firms?

This paper investigates three capital structure decisions – leverage, debt maturity and the source of debt – in a simultaneous setting. Moreover, we investigate whether these choices are influenced by the involvement of banks in a firm. Our results based on a panel of Dutch firms show that bank relationships, measured by interlocking board memberships and equity ownership, have a significant impact on the relations among the three capital structure choices. First, less bank involvement strengthens the positive impact of leverage on maturity. This is consistent with the liquidity risk theory, because involved banks help firms to mitigate liquidity risk. Second, bank debt negatively effects leverage in firms with bank interlocks, while this relation is absent in firms without such bank involvement. This result suggests that banks maximize the value of their loans by reducing overall leverage. Third, we find a strong trade-off between bank debt and maturity, which is independent of the degree of bank involvement

Ambient Intelligence and Persuasive Technology: The Blurring Boundaries Between Human and Technology

The currently developing fields of Ambient Intelligence and Persuasive Technology bring about a convergence of information technology and cognitive science. Smart environments that are able to respond intelligently to what we do and that even aim to influence our behaviour challenge the basic frameworks we commonly use for understanding the relations and role divisions between human beings and technological artifacts. After discussing the promises and threats of these technologies, this article develops alternative conceptions of agency, freedom, and responsibility that make it possible to better understand and assess the social roles of Ambient Intelligence and Persuasive Technology. The central claim of the article is that these new technologies urge us to blur the boundaries between humans and technologies also at the level of our conceptual and moral frameworks

Sparse Suffix and LCP Array:Simple, Direct, Small, and Fast

Sparse suffix sorting is the problem of sorting b = o(n) suffixes of a string of length n. Efficient sparse suffix sorting algorithms have existed for more than a decade. Despite the multitude of works and their justified claims for applications in text indexing, the existing algorithms have not been employed by practitioners. Arguably this is because there are no simple, direct, and efficient algorithms for sparse suffix array construction. We provide two new algorithms for constructing the sparse suffix and LCP arrays that are simultaneously simple, direct, small, and fast. In particular, our algorithms are: simple in the sense that they can be implemented using only basic data structures; direct in the sense that the output arrays are not a byproduct of constructing the sparse suffix tree or an LCE data structure; fast in the sense that they run in O(n log b) time, in the worst case, or in O(n) time, when the total number of suffixes with an LCP value greater than 2⌊log n/b⌋+1− 1 is in O(b/ log b), matching the time of optimal yet much more complicated algorithms [Gawrychowski and Kociumaka, SODA 2017; Birenzwige et al., SODA 2020]; and small in the sense that they can be implemented using only 8b + o(b) machine words. We also show that our second algorithm can be trivially amended to work in O(n) time for any uniformly random string. Our algorithms are non-trivial space-efficient adaptations of the Monte Carlo algorithm by I et al. for constructing the sparse suffix tree in O(n log b) time [STACS 2014]

Sparse Suffix and LCP Array: Simple, Direct, Small, and Fast

Sparse suffix sorting is the problem of sorting $b=o(n)$ suffixes of a string of length $n$. Efficient sparse suffix sorting algorithms have existed for more than a decade. Despite the multitude of works and their justified claims for applications in text indexing, the existing algorithms have not been employed by practitioners. Arguably this is because there are no simple, direct, and efficient algorithms for sparse suffix array construction. We provide two new algorithms for constructing the sparse suffix and LCP arrays that are simultaneously simple, direct, small, and fast. In particular, our algorithms are: simple in the sense that they can be implemented using only basic data structures; direct in the sense that the output arrays are not a byproduct of constructing the sparse suffix tree or an LCE data structure; fast in the sense that they run in $\mathcal{O}(n\log b)$ time, in the worst case, or in $\mathcal{O}(n)$ time, when the total number of suffixes with an LCP value greater than $2^{\lfloor \log \frac{n}{b} \rfloor + 1}-1$ is in $\mathcal{O}(b/\log b)$, matching the time of the optimal yet much more complicated algorithms [Gawrychowski and Kociumaka, SODA 2017; Birenzwige et al., SODA 2020]; and small in the sense that they can be implemented using only $8b+o(b)$ machine words. Our algorithms are simplified, yet non-trivial, space-efficient adaptations of the Monte Carlo algorithm by I et al. for constructing the sparse suffix tree in $\mathcal{O}(n\log b)$ time [STACS 2014]. We also provide proof-of-concept experiments to justify our claims on simplicity and efficiency.Comment: 16 pages, 1 figur

Adaptive Seeding for Gaussian Mixture Models

We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm. Thereby we aim to close the gap between simple random, e.g. uniform, and complex methods, that crucially depend on the right choice of hyperparameters. Our extensive experiments indicate the usefulness of our methods compared to common techniques and methods, which e.g. apply the original $K$-means++ and Gonzalez directly, with respect to artificial as well as real-world data sets.Comment: This is a preprint of a paper that has been accepted for publication in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016. The final publication is available at link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2 24