Reconsidering the European regulation of merchant transmission investment in light of the third energy package: The role of dominant generators

The regulation of merchant transmission investment (MTI) has become an important issue in the EU electricity sector, subsequent to the granting of authorizations by European authorities to five merchant projects: BritNed, Estlink, the East West Cables, NorGer and recently a merchant line connecting Italy and Austria. The creation of a new Agency for the Cooperation of Energy Regulators (ACER) at the EU level, which has decision-making powers on MTI, therefore presents a unique opportunity to question and re-design the current European policy. This paper shows that the recent decisions concerning MTI may suffer a strong bias against dominant electricity generators while incumbent Transmission System Operators (TSOs) or new entrant TSOs are generally favored by national regulators and the European Commission (EC). This strategy is misguided as it fails to recognize both the new incentives of generators to develop MTI and the conflict of interest between the regulated and non-regulated activities of incumbent TSOs. Letting dominant generators undertake MTI is indeed generally beneficial as long as potential abuses of dominance are mitigated. To deter possible anti-competitive effects, we propose a new and feasible allocation of regulatory powers based on a clear demarcation between the market monitoring powers of ACER and the antitrust powers of the EC.     

Optimizing Environment Maps for Material Depiction

We present an automated system for optimizing and synthesizing environment maps that enhance the appearance of materials in a scene. We first identify a set of lighting design principles for material depiction. Each principle specifies the distinctive visual features of a material and describes how environment maps can emphasize those features. We express these principles as linear or quadratic image quality metrics, and present a general optimization framework to solve for the environment map that maximizes these metrics. We accelerate metric evaluation using an approach dual to precomputed radiance transfer (PRT). In contrast to standard PRT that integrates light transport over the lighting domain to generate an image, we pre‐integrate light transport over the image domain to optimize for lighting. Finally we present two techniques for transforming existing photographic environment maps to better emphasize materials. We demonstrate the effectiveness of our approach by generating environment maps that enhance the depiction of a variety of materials including glass, metal, plastic, marble and velvet.     

The 3D Line Motion Matrix and Alignment of Line Reconstructions

We study the problem of aligning two 3D line reconstructions in projective, affine, metric or Euclidean space.We introduce the 6 × 6 3D line motion matrix that acts on Plücker coordinates. We characterize its algebraic properties and its relation to the usual 4 × 4 point motion matrix, and propose various methods for estimating 3D motion from line correspondences, based on cost functions defined in images or 3D space. We assess the quality of the different estimation methods using simulated data and real images.     

Monocular Template-based Reconstruction of Inextensible Surfaces

We present a monocular 3D reconstruction algorithm for inextensible deformable surfaces. It uses point correspondences between a single image of the deformed surface taken by a camera with known intrinsic parameters and a template. The main assumption we make is that the surface shape as seen in the template is known. Since the surface is inextensible, its deformations are isometric to the template. We exploit the distance preservation constraints to recover the 3D surface shape as seen in the image. Though the distance preservation constraints have already been investigated in the literature, we propose a new way to handle them. Spatial smoothness priors are easily incorporated, as well as temporal smoothness priors in the case of reconstruction from a video. The reconstruction can be used for 3D augmented reality purposes thanks to a fast implementation. We report results on synthetic and real data. Some of them are compared to stereo-based 3D reconstructions to demonstrate the efficiency of our method.     

Groupwise geometric and photometric direct image registration

Image registration consists in estimating geometric and photometric transformations that align two images as best as possible. The direct approach consists in minimizing the discrepancy in the intensity or color of the pixels. The inverse compositional algorithm has been recently proposed by Baker et al. for the direct estimation of groupwise geometric transformations. It is efficient in that it performs several computationally expensive calculations at a pre-computation phase. Photometric transformations act on the value of the pixels. They account for effects such as lighting change. Jointly estimating geometric and photometric transformations is thus important for many tasks such as image mosaicing. We propose an algorithm to jointly estimate groupwise geometric and photometric transformations while preserving the efficient pre-computation based design of the original inverse compositional algorithm. It is called the dual inverse compositional algorithm. It uses different approximations than the simultaneous inverse compositional algorithm and handles groupwise geometric and global photometric transformations. Its name stems from the fact that it uses an inverse compositional update rule for both the geometric and the photometric transformations. We demonstrate the proposed algorithm and compare it to previous ones on simulated and real data. This shows clear improvements in computational efficiency and in terms of convergence.     

A Hybrid approach for integer programming combining genetic algorithms, linear programming and ordinal optimization

Hybrid methods are promising tools in integer programming, as they combine the best features of different methods in a complementary fashion. This paper presents such a framework, integrating the notions of genetic algorithm, linear programming, and ordinal optimization in an effort to shorten computation times for large and/or difficult integer programming problems. Capitalizing on the central idea of ordinal optimization and on the learning capability of genetic algorithms to quickly generate good feasible solutions, and then using linear programming to solve the problem that results from fixing the integer part of the solution, one may be able to obtain solutions that are close to optimal. Indeed ordinal optimization guarantees the quality of the solutions found. Numerical testing on a real-life complex scheduling problem demonstrates the effectiveness and efficiency of this approach.     

An improved algorithm for the control synthesis of nonlinear sampled switched systems

A novel algorithm for the control synthesis for nonlinear switched systems is presented in this paper. Based on an existing procedure of state-space bisection and made available for nonlinear systems with the help of guaranteed integration, the algorithm has been improved to be able to consider longer patterns of modes with a better pruning approach. Moreover, the use of guaranteed integration also permits to take bounded perturbations and varying parameters into account. It is particularly interesting for safety critical applications, such as in aeronautical, military or medical fields. The whole approach is entirely guaranteed and the induced controllers are correct-by-design. Some experimentations are performed to show the important gain of the new algorithm.     

Multi-class segmentation of free-form online documents with tree conditional random fields

We present a new system for predicting the segmentation of online handwritten documents into multiple blocks, such as text paragraphs, tables, graphics, or mathematical expressions. A hierarchical representation of the document is adopted by aggregating strokes into blocks, and interactions between different levels are modeled in a tree Conditional Random Field. Features are extracted, and labels are predicted at each tree level with logistic classifiers, and Belief Propagation is adopted for optimal inference over the structure. Being fully trainable, the system is shown to properly handle difficult segmentation problems arising in unconstrained online note-taking documents, where no prior knowledge is available regarding the layout or the expected content. Our experiments show very promising results and allow to envision fully automatic segmentation of free-form online notes.