Preserving today for tomorrow: a case study of an archive of Interactive Music Installations

Περιέχει το πλήρες κείμενοThis work presents the problems addressed and the first results obtained by a project aimed at the preservation of Interactive Music Installations (IMI). Preservation requires that besides all the necessary components for the (re)production of a performance, also the knowledge about these components is kept, so that the original process can be repeated at any given time. This work proposes a multilevel approach for the preservation of IMI. As case studies, the Pinocchio Square (installed in EXPO 2002) and the Il Caos delle Sfere are considered

CVT/GPL phase 3 integrated testing

The hardware for 20 candidate shuttle program life sciences experiments was installed in the GPL and experiments were conducted during a 5-day simulated mission. The experiments involved humans, primates, rats, chickens, and marigold plants. All experiments were completed to the satisfaction of the experimenters. In addition to the scientific data gathered for each experiment, information was obtained concerning experiment hardware design and integration, experiment procedures, GPL support systems, and test operations. The results of the integrated tests are presented

End-to-end deep auto-encoder for segmenting a moving object with limited training data

Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples

INSPIRE Newsletter Fall 2018

https://scholarsmine.mst.edu/inspire-newsletters/1003/thumbnail.jp

WEATHERING THE STORM: DIAGNOSTIC MONITORING FOR PREVENTIVE CONSERVATION AT SPRUCE TREE HOUSE, MESA VERDE NATIONAL PARK, COLORADO

This paper examines Spruce Tree House, an alcovate site located in Mesa Verde National Park, as a model for monitoring the impacts of the natural environment on the deterioration of a unique collection of archaeological resources. At present, it is the only alcove site in the park that has been fully documented by park service archaeologists. In the past, preservation at Mesa Verde has focused on the minimal remedial stabilization of the prehistoric masonry structures. A comprehensive study of broad deterioration patterns across the alcovate sites in the park and the possible causes of these patterns has never been performed. Identifying common sources of deterioration site-wide has the potential to lead to a comprehensive maintenance plan that could slow deterioration through preventive as well as remedial actions, thereby protecting the resources and decreasing the amount of emergency rehabilitation work needed. Implementation of a monitoring program is the first step in this process, as it allows us to identify patterns and establish causality, leading eventually to non-invasive preventive and protective measures. This paper proposes that the presence of water is the main cause of deterioration at the site. A specific monitoring program has been put forth in order to verify that this is still an active problem at the site. The program includes the use of soil moisture meters, motion activated cameras and at least one weather station to record the external environmental conditions for comparison with occurrences within the alcove

SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly because of learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn the realistic motion coefficients, we explicitly model the connections between audio and different types of motion coefficients individually. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces. As for the head pose, we design PoseVAE via a conditional VAE to synthesize head motion in different styles. Finally, the generated 3D motion coefficients are mapped to the unsupervised 3D keypoints space of the proposed face render, and synthesize the final video. We conduct extensive experiments to show the superior of our method in terms of motion and video quality.Comment: Project page: https://sadtalker.github.i