Co-creating a repository of best-practices for collaborative translation

Collaborative translation has the potential for significantly changing how we translate content. However, successful deployment of this kind of approach is far from trivial, as it presents potential adopters with a rich and complex envelope of processes and technologies, whose respective impacts are still poorly understood. The present paper aims at facilitating this kind of decision making, by describing and cataloguing current best-practices in collaborative translation. More precisely, we present a collection of Design Patterns which was created collectively by a small group of practitioners, at a one-day roundtable hosted by the Translation Automation Users Society in October of 2011. This collection has been put on an open wiki site (www.collaborative-translation-patterns.com) in the hopes that other practitioners in the field will refine and augment it.

Forward Entrainment: Evidence, Controversies, Constraints, and Mechanisms

We define forward entrainment as that part of the entrainment process that outlasts the entraining stimulus. In this study, we examine conditions under which one may or may not observe forward entrainment. In part 1, we review and evaluate studies that have observed forward entrainment using a variety of psychophysical methods (detection, discrimination and reaction times), different target stimuli (tones, noise, gaps), different entraining sequences (sinusoidal, rectangular or sawtooth waveforms), a variety of physiological measures (MEG, EEG, ECoG, CSD), in different modalities (auditory and visual), across modalities (audiovisual and auditory-motor), and in different species. In part 2, we review those studies that have failed to observe forward entrainment, with emphasis on evaluating the methodological and stimulus design differences that may clarify the contrasting findings across these two classes of studies. In part 3, we describe those experimental conditions under which we ourselves have failed to observe forward entrainment, and provide new data on use of complex envelope patterns as entraining stimuli, show data on intersubject variability, and provide new findings on psychometric functions that characterize the strength of forward entrainment at different SNRs. In part 4 we theorize on potential mechanisms, describe how neurophysiological and psychophysical studies approach the study of entrainment, and caution against drawing direct causal inferences between the two without compelling evidence beyond correlative measures.

Algorithms of distortion compensation for broadband signals propagated through satellite ionospheric channels.

Methods for describing of digital signal complex envelope distortions due to influence of satellite ionosphere radiolines based on linear filtering methods are presented. Distortions of the phase-frequency characteristics of the digital signal envelopes due to the dispersion properties of the ionosphere cause time scattering and the occurrence of intersymbol interference, which reduce the reliability of communication. This determines the relevance of the development of the computational procedure for processing digital signals that reduce the effectiveness of this type of interferences. The descriptions of the algorithms for compensation of these distortions based on the use of the broadband pilot-signals and the formation of an inverse linear filter are given. A useful property of the considered pilot-signals is the coincidence of their structure (frequency band, envelope type, manipulation law) with the structure of information digital signals. By means of computer simulations of this algorithms, the possibility of almost complete compensation of the considered distortions of wideband signals is shown.

A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.