Bilingual word alignment forms the foundation of current work on statistical machine translation. Standard wordalignment methods involve the use of probabilistic generative models that are complex to implement and slow to train. In this paper we show that it is possible to approach the alignment accuracy of the standard models using algorithms that are much faster, and in some ways simpler, bas...

http://ww ckelma rol was just passed a stress test with flying colors. So last November, when a gist at the Cleveland Clinic Foundation Hospital asked the then 49-year-old ed nurse to help demonstrate an experimental new cardiac scanner, neither the n nor Fackelmann expected to see anything out of the ordinary. The idea was to slide Fackelmann through the machine and show what finely detailed o...

This paper discusses generalization bounds for complex data learning which serve as a theoretical foundation for complex support vector machine (SVM). Drawn on the generalization bounds, a complex SVM approach based on the Szegő kernel of the Hardy space H(D) is formulated. It is applied to the frequency-domain identification problem of discrete linear time-invariant system (LTIS). Experiments ...

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in differing images. The algorithm was first proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation fo...

This paper provides an overview on the advances of music information retrieval in symbolic representation of music. Such musical aspects as key, tonality, bass, melody, dynamics, rhythm and patterns are considered as foundation for visualizing systems for the piano stream in classical music. The paper then describes the proposed visualizing system and Malinowski’s music animation machine. It la...

High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide ev...

This paper presents a fast but yet simple solution to create areas on metrical occupancy grid maps which can easily be converted to topological maps. Those maps with their areas provide an understandable “non-expert” view on a robots environment and allow semantic labeling. This serves as a foundation for supporting navigation tasks like path planning, localization and human-machine-interaction...

This paper describes an approach to Machine Translation that places linguistic information at its foundation. The difficulty of translation from English to Japanese is illustrated with data that shows the influence of various linguistic contextual factors. Next, a method for natural language transfer is presented that integrates translation examples (represented as typed feature structures with...

Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in parallel (and possibly interacting) have summed time-evolution operators. From this foundation, algorithms for simulation, inference and model reduction may ...

Random Projection is a foundational research topic that connects a bunch of machine learning algorithms under a similar mathematical basis. It is used to reduce the dimensionality of the dataset by projecting the data points efficiently to a smaller dimensions while preserving the original relative distance between the data points. In this paper, we are intended to explain random projection met...