On the interpretation of wh-clauses in exclamative environments

In this paper, a class of sentences in German is discussed that are often called whexclamatives. […] So called wh-exclamatives can be roughly characterized as wh-clauses that are embedded under exclamative predicates like erstaunt sein/to be amazed at [...] or that are used as the basis for an exclamation [...]. One can ask if wh-exclamatives are a clause-type of their own, in particular, whether they are different from wh-clauses in question environments, that is under question predicates like to ask or to wonder or used as questions. It is often assumed that wh-clauses in exclamative contexts, both embedded and unembedded, are indeed different from wh-clauses in interrogative or question environments [...], at least regarding their semantical type, see for example Elliot (1971, 1974), Grimshaw (1979, 1981), Zaefferer (1983, 1984), Altmann (1 987, 1993). […] I assume with Grimshaw (1979) that so called wh-exclamatives and wh-interrogatives are alike with respect to their syntactical properties. In addition, I think that they are also alike semantically. So, what I like to do here is to evaluate the following hypothesis: So-called wh-exclamatives are of the same semantical type as wh-interrogatives

Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms

The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a numerical optimization problem. In this context, Stochastic Gradient Descent (SGD) methods have long proven to provide good results, both in terms of convergence and accuracy. Recently, several parallelization approaches have been proposed in order to scale SGD to solve very large ML problems. At their core, most of these approaches are following a map-reduce scheme. This paper presents a novel parallel updating algorithm for SGD, which utilizes the asynchronous single-sided communication paradigm. Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent (ASGD) provides faster (or at least equal) convergence, close to linear scaling and stable accuracy

Balancing the Communication Load of Asynchronously Parallelized Machine Learning Algorithms

Stochastic Gradient Descent (SGD) is the standard numerical method used to solve the core optimization problem for the vast majority of machine learning (ML) algorithms. In the context of large scale learning, as utilized by many Big Data applications, efficient parallelization of SGD is in the focus of active research. Recently, we were able to show that the asynchronous communication paradigm can be applied to achieve a fast and scalable parallelization of SGD. Asynchronous Stochastic Gradient Descent (ASGD) outperforms other, mostly MapReduce based, parallel algorithms solving large scale machine learning problems. In this paper, we investigate the impact of asynchronous communication frequency and message size on the performance of ASGD applied to large scale ML on HTC cluster and cloud environments. We introduce a novel algorithm for the automatic balancing of the asynchronous communication load, which allows to adapt ASGD to changing network bandwidths and latencies.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0495

Dry Friction in the Frenkel-Kontorova-Tomlinson Model: Dynamical Properties

Wearless friction is investigated in a simple mechanical model called Frenkel-Kontorova-Tomlinson model. We have introduced this model in [Phys. Rev. B, Vol. 53, 7539 (1996)] where the static friction has already been considered. Here the model is treated for constant sliding speed. The kinetic friction is calculated numerically as well as analytically. As a function of the sliding velocity it shows many structures which can be understood by varies kinds of phonon resonances (normal, superharmonic and parametric) caused by the so-called "washboard wave". For increasing interaction strength the regular motion becomes chaotic (fluid-sliding state). The fluid sliding state is mainly determined by the density of decay channels of m washboard waves into n phonons. We also find strong bistabilities and coherent motions with superimposed dark envelope solitons which interact nondestructively.Comment: Written in RevTeX, figures in PostScript, appears in Z. Phys.