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PaRDeS, the journal of the German Association for Jewish Studies, aims at exploring the fruitful and multifarious cultures of Judaism as well as their relations to their environment within diverse areas of research. In addition, the journal promotes Jewish Studies within academic discourse and reflects on its historic and social responsibilities.
In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the Substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process
In this study we examine the tonal organization of a series of recordings of liturgical chants, sung in 1966 by the Georgian master singer Artem Erkomaishvili. This dataset is the oldest corpus of Georgian chants from which the time synchronous F0-trajectories for all three voices have been reliably determined (Müller et al. 2017). It is therefore of outstanding importance for the understanding of the tuning principles of traditional Georgian vocal music.
The aim of the present study is to use various computational methods to analyze what these recordings can contribute to the ongoing scientific dispute about traditional Georgian tuning systems. Starting point for the present analysis is the re-release of the original audio data together with estimated fundamental frequency (F0) trajectories for each of the three voices, beat annotations, and digital scores (Rosenzweig et al. 2020). We present synoptic models for the pitch and the harmonic interval distributions, which are the first of such models for which the complete Erkomaishvili dataset was used. We show that these distributions can be very compactly be expressed as Gaussian mixture models, anchored on discrete sets of pitch or interval values for the pitch and interval distributions, respectively. As part of our study we demonstrate that these pitch values, which we refer to as scale pitches, and which are determined as the mean values of the Gaussian mixture elements, define the scale degrees of the melodic sound scales which build the skeleton of Artem Erkomaishvili’s intonation. The observation of consistent pitch bending of notes in melodic phrases, which appear in identical form in a group of chants, as well as the observation of harmonically driven intonation adjustments, which are clearly documented for all pure harmonic intervals, demonstrate that Artem Erkomaishvili intentionally deviates from the scale pitch skeleton quite freely. As a central result of our study, we proof that this melodic freedom is always constrained by the attracting influence of the scale pitches. Deviations of the F0-values of individual note events from the scale pitches at one instance of time are compensated for in the subsequent melodic steps. This suggests a deviation-compensation mechanism at the core of Artem Erkomaishvili’s melody generation, which clearly honors the scales but still allows for a large degree of melodic flexibility. This model, which summarizes all partial aspects of our analysis, is consistent with the melodic scale models derived from the observed pitch distributions, as well as with the melodic and harmonic interval distributions. In addition to the tangible results of our work, we believe that our work has general implications for the determination of tuning models from audio data, in particular for non-tempered music.
fundamental challenge for product-lifecycle management in collaborative value networks is to utilize the vast amount of product information available from heterogeneous sources in order to improve business analytics, decision support, and processes. This becomes even more challenging if those sources are distributed across multiple organizations. Federations of semantic information services, combining service-orientation and semantic technologies, provide a promising solution for this problem. However, without proper measures to establish information security, companies will be reluctant to join an information federation, which could lead to serious adoption barriers.
Following the design science paradigm, this paper presents general objectives and a process for designing a secure federation of semantic information services. Furthermore, new as well as established security measures are discussed. Here, our contributions include an access-control enforcement system for semantic information services and a process for modeling access-control policies across organizations. In addition, a comprehensive security architecture is presented. An implementation of the architecture in the context of an application scenario and several performance experiments demonstrate the practical viability of our approach.
High-performance numerical codes are an indispensable tool for hydrogeologists when modeling subsurface flow and transport systems. But as they are written in compiled languages, like C/C++ or Fortran, established software packages are rarely user-friendly, limiting a wider adoption of such tools. OpenGeoSys (OGS), an open-source, finite-element solver for thermo-hydro-mechanical-chemical processes in porous and fractured media, is no exception. Graphical user interfaces may increase usability, but do so at a dramatic reduction of flexibility and are difficult or impossible to integrate into a larger workflow. Python offers an optimal trade-off between these goals by providing a highly flexible, yet comparatively user-friendly environment for software applications. Hence, we introduceogs5py, a Python-API for the OpenGeoSys 5 scientific modeling package. It provides a fully Python-based representation of an OGS project, a large array of convenience functions for users to interact with OGS and connects OGS to the scientific and computational environment of Python.
The transmembrane tight junction protein occludin is sensitive to oxidative stress. Occludin oligomerizes; however, its function in the tight junction is unknown. The cytosolic C-terminal tail contains a coiled coil-domain and forms dimers contributing to the oligomerization. The regulation of the oligomerization remains unclear. As the domain area contains sulfhydryl residues, we tested the hypothesis that the dimerization of the coiled coil-domain depends on these residues. We showed that the dimerization is modulated by the thiol concentration in the low-millimolar range, which is relevant both for physiological and pathophysiological conditions. Masking the sulfhydryl residues in the fragment by covalent binding of 4-vinyl pyridine prevented the dimerization but did not affect its helical structure and cylindric shape. The data demonstrate, for the first time, that disulfide bridge formation of murine cystein 408 is involved in the dimerization. This process is redox-sensitive but the secondary structure of the domain is not. It is concluded that the dimerization of occludin may play a regulatory role in the tight junction assembly under physiological and pathological conditions.
Information integration across company borders becomes increasingly important for the success of product lifecycle management in industry and complex supply chains. Semantic technologies are about to play a crucial role in this integrative process. However, cross-company data exchange requires mechanisms to enable fine-grained access control definition and enforcement, preventing unauthorized leakage of confidential data across company borders. Currently available semantic repositories are not sufficiently equipped to satisfy this important requirement. This paper presents an infrastructure for controlled sharing of semantic data between cooperating business partners. First, we motivate the need for access control in semantic data federations by a case study in the industrial service sector. Furthermore, we present an architecture for controlling access to semantic repositories that is based on our newly developed SemForce security service. Finally, we show the practical feasibility of this architecture by an implementation and several performance experiments.
Robust ensemble learning
(2000)
SVM and boosting : one class
(2000)