Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.Comment: 10 page

Advanced actuators for the control of large space structures

The objective of this research was to develop advanced six-degree-of-freedom actuators employing magnetic suspensions suitable for the control of structural vibrations in large space structures. The advanced actuators consist of a magnetically suspended mass that has three-degrees-of-freedom in both translation and rotation. The most promising of these actuators featured a rotating suspended mass providing structural control torques in a manner similar to a control moment gyro (CMG). These actuators employ large-angle-magnetic suspensions that allow gimballing of the suspended mass without mechanical gimbals. Design definitions and sizing algorithms for these CMG type as well as angular reaction mass actuators based on multi-degree-of-freedom magnetic suspensions were developed. The performance of these actuators was analytically compared with conventional reaction mass actuators for a simple space structure model

Generalized analytic model for rotational and anisotropic metasolids

An analytical approach is presented to model a metasolid accounting for anisotropic effects and rotational mode. The metasolid is made of either cylindrical or spherical hard inclusions embedded in a stiff matrix via soft claddings, and the analytical approach to study the composite material is a generalization of the method introduced by Liu \textit{et al.} [Phys. Rev. B, 71, 014103 (2005)]. It is shown that such a metasolid exhibits negative mass densities near the translational-mode resonances, and negative density of moment of inertia near the rotational resonances. The results obtained by this analytical and continuum approach are compared with those from discrete mass-spring model, and the validity of the later is discussed. Based on derived analytical expressions, we study how different resonance frequencies associated with different modes vary and are placed with respect to each other, in function of the mechanical properties of the coating layer. We demonstrate that the resonances associated with additional modes taken into account, that is, axial translation for cylinders, and rotations for both cylindrical and spherical systems, can occur at lower frequencies compared to the previously studied plane-translational modes.Comment: 30 pages, 10 figure

Development of the Universe and New Cosmology

Cosmology is undergoing an explosive period of activity, fueled both by new, accurate astrophysical data and by innovative theoretical developments. Cosmological parameters such as the total density of the Universe and the rate of cosmological expansion are being precisely measured for the first time, and a consistent standard picture of the Universe is beginning to emerge. Recent developments in cosmology give rise the intriguing possibility that all structures in the Universe, from superclusters to planets, had a quantum-mechanical origin in its earliest moments. Furthermore, these ideas are not idle theorizing, but predictive, and subject to meaningful experimental test. We review the concordance model of the development of the Universe, as well as evidence for the observational revolution that this field is going through. This already provides us with important information on particle physics, which is inaccessible to accelerators.Comment: 9 pages; The German translation of this mini review can be uploaded from http://www.cc.ethz.ch/bulletin/ New references are added with respective minor changes of tex

Massive Dimensionality Reduction for the Left Ventricular Mesh

Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model

Massive Dimensionality Reduction for the Left Ventricular Mesh

Statistical emulation is a promising approach for the translation of cardio-mechanical modelling into the clinical practice. However, a key challenge is to find a low-dimensional representation of the heart, or, for the specific purpose of diagnosing the risk of heart attacks, the left-ventricle of the heart. We consider the problem of dimensionality reduction of the left ventricular mesh, in which we investigate three classes of techniques: principal component analysis (PCA), deep learning (DL) methods based on auto-encoders, and a parametric model from the cardio-mechanical literature. Our finding is that PCA performs as well as the computationally more expensive DL methods, and both outperform the state-of-the-art parametric model