Brian A. Curran; Anthony Grafton; Pamela O. Long; Benjamin Weiss. Obelisk: A History

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Solving the TTC 2011 Reengineering Case with GrGen.NET

The challenge of the Reengineering Case is to extract a state machine model out of the abstract syntax graph of a Java program. The extracted state machine offers a reduced view on the full program graph and thus helps to understand the program regarding the question of interest. We tackle this task employing the general purpose graph rewrite system GrGen.NET (www.grgen.net).Comment: In Proceedings TTC 2011, arXiv:1111.440

Michael S. Mahoney, 1939–2008

Perhaps the clearest testimony to the scholarly range and depth of Princeton's now‐lamented Michael S. Mahoney lies in the dismay of his colleagues in the last few years, as they contemplated his imminent retirement. How to maintain coverage of his fields? Fretting over this question, the program in history of science that he did so much to build recently found itself sketching a five-year plan that involved replacing him with no fewer than four new appointments: a historian of mathematics with the ability to handle the course on Greek antiquity, a historian of the core problems of the Scientific Revolution, a historian of technology who could cover the nineteenth‐century United States and Britain, and, finally, a historian of the computer-and-media revolution. In his passing we have lost a small department

Incommensurability and Evidence

Incommensurability between successive scientific theories—the impossibility of empirical evidence dictating the choice between them—was Thomas Kuhn's most controversial proposal. Toward defending it, he directed much effort over his last 30 years into formulating precise conditions under which two theories would be undeniably incommensurable with one another. His first step, in the late 1960s, was to argue that incommensurability must result when two theories involve incompatible taxonomies. The problem he then struggled with, never obtaining a solution that he found entirely satisfactory, was how to extend this initial line of thought to sciences like physics in which taxonomy is not so transparently dominant as it is, for example, in chemistry. This paper reconsiders incommensurability in the light of examples in which evidence historically did and did not carry over continuously from old laws and theories to new ones. The transition from ray to wave optics early in the nineteenth century, we argue, is especially informative in this regard. The evidence for the theory of polarization within ray optics did not carry over to wave optics, so that this transition can be regarded as a prototypical case of discontinuity of evidence, and hence of incommensurability in the way Kuhn wanted. Yet the evidence for classic geometric optics did carry over to wave optics, notwithstanding the fundamental conceptual readjustment that Fresnel's wave theory required

An HDTV encoder for PALplus transmissions, supplemented with a digital residual signal

This paper discusses the design of a video encoder which will enable PALplus compatible HDTV transmission, by embedding a digital residual component within the standard analogue PALplus television signal. The design strategy includes a 2-D diagonal prefilter, together with a QMF band-splitting filter pair, from which the residual is initially derived. The performance of these filters is reviewed, especially in respect of the errors introduced by aliasing and crosstalk distortion. A number of different digital modulation techniques are then examined for the transmission of the residual signal and a qualitative appraisal of their effectiveness presente

A study of hierarchical and flat classification of proteins

Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article we investigate empirically whether this is the case for two such hierarchies. We compare multi-class classification techniques that exploit the information in those class hierarchies and those that do not, using logistic regression, decision trees, bagged decision trees, and support vector machines as the underlying base learners. In particular, we compare hierarchical and flat variants of ensembles of nested dichotomies. The latter have been shown to deliver strong classification performance in multi-class settings. We present experimental results for synthetic, fold recognition, enzyme classification, and remote homology detection data. Our results show that exploiting the class hierarchy improves performance on the synthetic data, but not in the case of the protein classification problems. Based on this we recommend that strong flat multi-class methods be used as a baseline to establish the benefit of exploiting class hierarchies in this area

Fast conditional density estimation for quantitative structure-activity relationships

Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional methods for conditional density estimation, they are based on generic machine learning schemes, more specifically, class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training. Therefore, generic machine-learning based methods for conditional density estimation may be a good and fast option for quantifying uncertainty in QSAR modeling.http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/181

Outside board memberships of CEOs: Expertise or entrenchment?

We investigate whether outside board memberships of CEOs signal expertise or entrenchment. The analysis is based on panel data of the largest German companies covering the period from 1996 to 2008. Supporting the entrenchment hypothesis, our analysis reveals that firms having a CEO with one or more outside mandates suffer from significantly weaker firm performance compared with firms having a CEO without any outside board mandates. Moreover, disciplinary CEO turnovers become less likely and turnover-performance sensitivity declines with rising board memberships of the top manager. We conclude that outside mandates enhance managerial power at the expense of the home firm's shareholders. --Corporate Governance,Entrenchment,Outside Board Memberships,CEO turnover

Solving the TTC 2011 Compiler Optimization Task with metatools

The authors' "metatools" are a collection of tools for generic programming. This includes generating Java sources from mathematically well-founded specifications, as well as the creation of strictly typed document object models for XML encoded texts. In this context, almost every computer-internal structure is treated as a "model", and every computation is a kind of model transformation. This concept differs significantly from "classical model transformation" executed by specialized tools and languages. Therefore it seemed promising to the organizers of the TTC 2011, as well as to the authors, to apply metatools to one of the challenges, namely to the "compiler optimization task". This is a report on the resulting experiences.Comment: In Proceedings TTC 2011, arXiv:1111.440