musrfit: A free platform-independent framework for muSR data analysis

A free data-analysis framework for muSR has been developed. musrfit is fully written in C++, is running under GNU/Linux, Mac OS X, as well as Microsoft Windows, and is distributed under the terms of the GNU GPL. It is based on the CERN ROOT framework and is utilizing the Minuit optimization routines for fitting. It consists of a set of programs allowing the user to analyze and visualize the data. The fitting process is controlled by an ascii-input file with an extended syntax. A dedicated text editor is helping the user to create and handle these files in an efficient way, execute the fitting, show the data, get online help, and so on. A versatile tool for the generation of new input files and the extraction of fit parameters is provided as well. musrfit facilitates a plugin mechanism allowing to invoke user-defined functions. Hence, the functionality of the framework can be extended with a minimal amount of overhead for the user. Currently, musrfit can read the following facility raw-data files: PSI-BIN, MDU (PSI), ROOT (LEM/PSI), WKM (outdated ascii format), MUD (TRIUMF), NeXus (ISIS).Comment: 4 pages, 4 figure

On the Communication of Scientific Results: The Full-Metadata Format

In this paper, we introduce a scientific format for text-based data files, which facilitates storing and communicating tabular data sets. The so-called Full-Metadata Format builds on the widely used INI-standard and is based on four principles: readable self-documentation, flexible structure, fail-safe compatibility, and searchability. As a consequence, all metadata required to interpret the tabular data are stored in the same file, allowing for the automated generation of publication-ready tables and graphs and the semantic searchability of data file collections. The Full-Metadata Format is introduced on the basis of three comprehensive examples. The complete format and syntax is given in the appendix

Disentanglement of Syntactic Components for Text Generation

Modelling human generated text, i.e., natural language data, is an important challenge in artificial intelligence. A good AI program should be able to understand and analyze natural language, and generate fluent and accurate responses. This standard is seen in applications of AI for natural language like machine translation, summarization, and dialog generation, all of which require the above ability. This work examines the application of deep neural networks for natural language generation. We explore how graph convolutional networks (GCNs) can be paired with recurrent neural networks (RNNs) for text generation. GCNs have the advantage of being able to leverage the inherent graphical nature of text. Sentences can be expressed as dependency trees, and GCNs can incorporate this information to generate sentences in a syntax-aware manner. Modelling sentences with both dependency trees and word representations allows us to disentangle the syntactic components of sentences and generate sentences while fusing parts of speech from multiple sentences. Our methodology combines the sentence representations from an RNN with that of a GCN to allow a decoder to gain syntactic information while reconstructing a sentence. We explore different ways of separating the syntax components in a sentence and inspect how the generation operates. We report BLEU and perplexity scores to evaluate how well the model incorporates the content based on its syntax from multiple sentences. We also observe, qualitatively, how the model generates fluent and coherent sentences while assimilating syntactic components from multiple sentences