Overcoming timescale and finite-size limitations to compute nucleation rates from small scale Well Tempered Metadynamics simulations

Condensation of a liquid droplet from a supersaturated vapour phase is initiated by a prototypical nucleation event. As such it is challenging to compute its rate from atomistic molecular dynamics simulations. In fact at realistic supersaturation conditions condensation occurs on time scales that far exceed what can be reached with conventional molecular dynamics methods. Another known problem in this context is the distortion of the free energy profile associated to nucleation due to the small, finite size of typical simulation boxes. In this work the problem of time scale is addressed with a recently developed enhanced sampling method while contextually correcting for finite size effects. We demonstrate our approach by studying the condensation of argon, and showing that characteristic nucleation times of the order of magnitude of hours can be reliably calculated, approaching realistic supersaturation conditions, thus bridging the gap between what standard molecular dynamics simulations can do and real physical systems.Comment: 9 pages, 7 figures, additional figures and data provided as supplementary information. Submitted to the Journal of Chemical Physisc

Adaptive Nonlocal Signal Restoration and Enhancement Techniques for High-Dimensional Data

The large number of practical applications involving digital images has motivated a significant interest towards restoration solutions that improve the visual quality of the data under the presence of various acquisition and compression artifacts. Digital images are the results of an acquisition process based on the measurement of a physical quantity of interest incident upon an imaging sensor over a specified period of time. The quantity of interest depends on the targeted imaging application. Common imaging sensors measure the number of photons impinging over a dense grid of photodetectors in order to produce an image similar to what is perceived by the human visual system. Different applications focus on the part of the electromagnetic spectrum not visible by the human visual system, and thus require different sensing technologies to form the image. In all cases, even with the advance of technology, raw data is invariably affected by a variety of inherent and external disturbing factors, such as the stochastic nature of the measurement processes or challenging sensing conditions, which may cause, e.g., noise, blur, geometrical distortion and color aberration. In this thesis we introduce two filtering frameworks for video and volumetric data restoration based on the BM3D grouping and collaborative filtering paradigm. In its general form, the BM3D paradigm leverages the correlation present within a nonlocal emph{group} composed of mutually similar basic filtering elements, e.g., patches, to attain an enhanced sparse representation of the group in a suitable transform domain where the energy of the meaningful part of the signal can be thus separated from that of the noise through coefficient shrinkage. We argue that the success of this approach largely depends on the form of the used basic filtering elements, which in turn define the subsequent spectral representation of the nonlocal group. Thus, the main contribution of this thesis consists in tailoring specific basic filtering elements to the the inherent characteristics of the processed data at hand. Specifically, we embed the local spatial correlation present in volumetric data through 3-D cubes, and the local spatial and temporal correlation present in videos through 3-D spatiotemporal volumes, i.e. sequences of 2-D blocks following a motion trajectory. The foundational aspect of this work is the analysis of the particular spectral representation of these elements. Specifically, our frameworks stack mutually similar 3-D patches along an additional fourth dimension, thus forming a 4-D data structure. By doing so, an effective group spectral description can be formed, as the phenomena acting along different dimensions in the data can be precisely localized along different spectral hyperplanes, and thus different filtering shrinkage strategies can be applied to different spectral coefficients to achieve the desired filtering results. This constitutes a decisive difference with the shrinkage traditionally employed in BM3D-algorithms, where different hyperplanes of the group spectrum are shrunk subject to the same degradation model. Different image processing problems rely on different observation models and typically require specific algorithms to filter the corrupted data. As a consequent contribution of this thesis, we show that our high-dimensional filtering model allows to target heterogeneous noise models, e.g., characterized by spatial and temporal correlation, signal-dependent distributions, spatially varying statistics, and non-white power spectral densities, without essential modifications to the algorithm structure. As a result, we develop state-of-the-art methods for a variety of fundamental image processing problems, such as denoising, deblocking, enhancement, deflickering, and reconstruction, which also find practical applications in consumer, medical, and thermal imaging

Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations

While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations. In this work, we introduce the first 3D-based multi-frame denoising method that significantly outperforms its 2D-based counterparts with lower computational requirements. Our method extends the multiplane image (MPI) framework for novel view synthesis by introducing a learnable encoder-renderer pair manipulating multiplane representations in feature space. The encoder fuses information across views and operates in a depth-wise manner while the renderer fuses information across depths and operates in a view-wise manner. The two modules are trained end-to-end and learn to separate depths in an unsupervised way, giving rise to Multiplane Feature (MPF) representations. Experiments on the Spaces and Real Forward-Facing datasets as well as on raw burst data validate our approach for view synthesis, multi-frame denoising, and view synthesis under noisy conditions.Comment: Accepted at CVPR 202

Video Filtering Using Separable Four-Dimensional Nonlocal Spatiotemporal Transforms

The large number of practical application involving digital videos has motivated a significant interest in restoration or enhancement solutions to improve the visual quality under the presence of noise. We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences to reduce the effects of noise. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a four-dimensional transform- domain representation is leveraged to enforce sparsity and thus regularize the data. Moreover we present an extension of our algorithm that can be effectively used as a deblocking and deringing filter to reduce the artifacts introduced by most of the popular video compression techniques. Our algorithm, termed V-BM4D, at first constructs three-dimensional volumes, by tracking blocks along trajectories defined by the motion vectors, and then groups together mutually similar volumes by stacking them along an additional fourth dimension. Each group is transformed through a decorrelating four-dimensional separable transform, and then it is collaboratively filtered by coeffcients shrinkage. The effectiveness of shrinkage is due to the sparse representation of the transformed group. Sparsity is achieved because of different type of correlation among the groups: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation along the fourth dimension. As a conclusive step, the different estimates of the filtered groups are adaptively aggregated and subsequently returned to their original position, to produce a final estimate of the original video. The proposed filtering procedure leads to excellent results in both objective and subjective visual quality, since in the restored video sequences the effect of the noise or of the compression artifacts is noticeably reduced, while the significant features are preserved. As demonstrated by experimental results, V-BM4D outperforms the state of the art in video denoising. /Kir1

The global operations of European firms - The second EFIGE policy report

This Bruegel blueprint analyses, within the framework of the EFIGE (European Firms in a Global Economy) project, the export and foreign investment performance of European firms. It is based on new cross-country data from 15,000 individual firms never available before. Written by Giorgio Barba Navaretti, Matteo Bugamelli, Gianmarco Ottaviano and Fabiano Schivardi, the report looks at the specific elements that make some European companies more competitive than others in foreign markets, revealing that firm characteristics -mainly size- are the primary determinants of export performance, even more so than country characteristics. Therefore the authors suggest that firm growth and consolidation in all European countries would generate a considerable increase in the value of European exports and thus help lift European growth. These findings will be crucial for policymakers, who, in order to boost the chance of European firmsâ?? on foreign markets, should shift the policy discussion from the current focus on specific sectors and skill groups to structural reforms that allow firms across the board to grow and to develop more sophisticated forms of management. Until now, evidence on European firmsâ?? competitiveness has been based on partial, non-comparable national data. But for the first time this paper is based on detailed results from a new large-scale survey of 15,000 manufacturing companies in seven EU countries (Austria, France, Germany, Hungary, Italy, Spain and the United Kingdom). The survey examines firmsâ?? exporting, importing, outsourcing and foreign investment activities. This survey data has then been combined with structural data about the individual firms taken from their balance-sheets such as governance, profits, number of employees.

IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014 (preprint) 1 Joint Removal of Random and Fixed-Pattern Noise through Spatiotemporal Video Filtering

Abstract—We propose a framework for the denoising of videos jointly corrupted by spatially correlated (i.e. non-white) random noise and spatially correlated fixed-pattern noise. Our approach is based on motion-compensated 3-D spatiotemporal volumes, i.e. a sequence of 2-D square patches extracted along the motion trajectories of the noisy video. First, the spatial and temporal correlations within each volume are leveraged to sparsify the data in 3-D spatiotemporal transform domain, and then the coefficients of the 3-D volume spectrum are shrunk using an adaptive 3-D threshold array. Such array depends on the particular motion trajectory of the volume, the individual power spectral densities of the random and fixed-pattern noise, and also the noise variances which are adaptively estimated in transform domain. Experimental results on both synthetically corrupted data and real infrared videos demonstrate a superior suppression of the random and fixed-pattern noise from both an objective and a subjective point of view. Index Terms—Video denoising, spatiotemporal filtering, fixedpattern noise, power spectral density, adaptive transforms, thermal imaging. I

Improved Signal Characterization via Empirical Mode Decomposition to Enhance in-line Quality Monitoring

The machine tool industry is facing the need to increase the sensorization of production systems to meet evolving market demands. This leads to the increasing interest for in-process monitoring tools that allow a fast detection of faults and unnatural process behaviours during the process itself. Nevertheless, the analysis of sensor signals implies several challenges. One major challenge consists of the complexity of signal patterns, which often exhibit a multiscale content, i.e., a superimposition of both stationary and non-stationary fluctuations on different time-frequency levels. Among time-frequency techniques, Empirical Mode Decomposition (EMD) is a powerful method to decompose any signal into its embedded oscillatory modes in a fully data-driven way, without any ex-ante basis selection. Because of this, it might be used effectively for automated monitoring and diagnosis of manufacturing processes. Unfortunately, it usually yields an over-decomposition, with single oscillation modes that can be split into more than one scale (this effect is also known as “mode mixing”). The literature lacks effective strategies to automatically synthetize the decomposition into a minimal number of physically relevant and interpretable components. This paper proposes a novel approach to achieve a synthetic decomposition of complex signals through the EMD procedure. A new criterion is proposed to group together multiple components associated to a common time-frequency pattern, aimed at summarizing the information content into a minimal number of modes, which may be easier to interpret. A real case study in waterjet cutting is presented, to demonstrate the benefits and the critical issues of the proposed approach