Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

Influence of high-conductivity buffer composition on field-enhanced sample injection coupled to sweeping in CE

The aim of this work was to clarify the mechanism taking place in field-enhanced sample injection coupled to sweeping and micellar EKC (FESI-Sweep-MEKC), with the utilization of two acidic high-conductivity buffers (HCBs), phosphoric acid or sodium phosphate buffer, in view of maximizing sensitivity enhancements. Using cationic model compounds in acidic media, a chemometric approach and simulations with SIMUL5 were implemented. Experimental design first enabled to identify the significant factors and their potential interactions. Simulation demonstrates the formation of moving boundaries during sample injection, which originate at the initial sample/HCB and HCB/buffer discontinuities and gradually change the compositions of HCB and BGE. With sodium phosphate buffer, the HCB conductivity increased during the injection, leading to a more efficient preconcentration by staking (about 1.6 times) than with phosphoric acid alone, for which conductivity decreased during injection. For the same injection time at constant voltage, however, a lower amount of analytes was injected with sodium phosphate buffer than with phosphoric acid. Consequently sensitivity enhancements were lower for the whole FESI-Sweep-MEKC process. This is why, in order to maximize sensitivity enhancements, it is proposed to work with sodium phosphate buffer as HCB and to use constant current during sample injection.

Dynamic high-resolution computer simulation of electrophoretic enantiomer separations with neutral cyclodextrins as chiral selectors.

GENTRANS, a comprehensive one-dimensional dynamic simulator for electrophoretic separations and transport, was extended for handling electrokinetic chiral separations with a neutral ligand. The code can be employed to study the 1:1 interaction of monovalent weak and strong acids and bases with a single monovalent weak or strong acid or base additive, including a neutral cyclodextrin, under real experimental conditions. It is a tool to investigate the dynamics of chiral separations and to provide insight into the buffer systems used in chiral capillary zone electrophoresis (CZE) and chiral isotachophoresis. Analyte stacking across conductivity and buffer additive gradients, changes of additive concentration, buffer component concentration, pH, and conductivity across migrating sample zones and peaks, and the formation and migration of system peaks can thereby be investigated in a hitherto inaccessible way. For model systems with charged weak bases and neutral modified β-cyclodextrins at acidic pH, for which complexation constants, ionic mobilities, and mobilities of selector-analyte complexes have been determined by CZE, simulated and experimentally determined electropherograms and isotachopherograms are shown to be in good agreement. Simulation data reveal that CZE separations of cationic enantiomers performed in phosphate buffers at low pH occur behind a fast cationic migrating system peak that has a small impact on the buffer composition under which enantiomeric separation takes place.

Context-aware Adaptation Mechanism for Video Dissemination over Flying Ad-Hoc Networks

The user experience on watching live video se- quences transmitted over a Flying Ad-Hoc Networks (FANETs) must be considered to drop packets in overloaded queues, in scenarios with high buffer overflow and packet loss rate. In this paper, we introduce a context-aware adaptation mechanism to manage overloaded buffers. More specifically, we propose a utility function to compute the dropping probability of each packet in overloaded queues based on video context information, such as frame importance, packet deadline, and sensing relevance. In this way, the proposed mechanism drops the packet that adds the minimum video distortion. Simulation evaluation shows that the proposed adaptation mechanism provides real-time multimedia dissemination with QoE support in a multi-hop, multi-flow, and mobile network environments.

Image Space Adaptive Rendering

In this thesis, we develop an adaptive framework for Monte Carlo rendering, and more specifically for Monte Carlo Path Tracing (MCPT) and its derivatives. MCPT is attractive because it can handle a wide variety of light transport effects, such as depth of field, motion blur, indirect illumination, participating media, and others, in an elegant and unified framework. However, MCPT is a sampling-based approach, and is only guaranteed to converge in the limit, as the sampling rate grows to infinity. At finite sampling rates, MCPT renderings are often plagued by noise artifacts that can be visually distracting. The adaptive framework developed in this thesis leverages two core strategies to address noise artifacts in renderings: adaptive sampling and adaptive reconstruction. Adaptive sampling consists in increasing the sampling rate on a per pixel basis, to ensure that each pixel value is below a predefined error threshold. Adaptive reconstruction leverages the available samples on a per pixel basis, in an attempt to have an optimal trade-off between minimizing the residual noise artifacts and preserving the edges in the image. In our framework, we greedily minimize the relative Mean Squared Error (rMSE) of the rendering by iterating over sampling and reconstruction steps. Given an initial set of samples, the reconstruction step aims at producing the rendering with the lowest rMSE on a per pixel basis, and the next sampling step then further reduces the rMSE by distributing additional samples according to the magnitude of the residual rMSE of the reconstruction. This iterative approach tightly couples the adaptive sampling and adaptive reconstruction strategies, by ensuring that we only sample densely regions of the image where adaptive reconstruction cannot properly resolve the noise. In a first implementation of our framework, we demonstrate the usefulness of our greedy error minimization using a simple reconstruction scheme leveraging a filterbank of isotropic Gaussian filters. In a second implementation, we integrate a powerful edge aware filter that can adapt to the anisotropy of the image. Finally, in a third implementation, we leverage auxiliary feature buffers that encode scene information (such as surface normals, position, or texture), to improve the robustness of the reconstruction in the presence of strong noise.

The implicit affiliation motive moderates cortisol responses to acute psychosocial stress in high school students

It has been previously shown that the implicit affiliation motive – the need to establish and maintain friendly relationships with others – leads to chronic health benefits. The underlying assumption for the present research was that the implicit affiliation motive also moderates the salivary cortisol response to acute psychological stress when some aspects of social evaluation and uncontrollability are involved. By contrast we did not expect similar effects in response to exercise as a physical stressor. Fifty-nine high school students aged M = 14.8 years were randomly assigned to a psychosocial stress (publishing the results of an intelligence test performed), a physical stress (exercise intensity of 65–75% of HRmax), and a control condition (normal school lesson) each lasting 15 min. Participants’ affiliation motives were assessed using the Operant Motive Test and salivary cortisol samples were taken pre and post stressor. We found that the strength of the affiliation motive negatively predicted cortisol reactions to acute psychosocial but not to physical stress when compared to a control group. The results suggest that the affiliation motive buffers the effect of acute psychosocial stress on the HPA axis.

Biodiversity increases the resistance of ecosystem productivity to climate extremes

It remains unclear whether biodiversity buffers ecosystems against climate extremes, which are becoming increasingly frequent worldwide. Early results suggested that the ecosystem productivity of diverse grassland plant communities was more resistant, changing less during drought, and more resilient, recovering more quickly after drought, than that of depauperate communities. However, subsequent experimental tests produced mixed results. Here we use data from 46 experiments that manipulated grassland plant diversity to test whether biodiversity provides resistance during and resilience after climate events. We show that biodiversity increased ecosystem resistance for a broad range of climate events, including wet or dry, moderate or extreme, and brief or prolonged events. Across all studies and climate events, the productivity of low-diversity communities with one or two species changed by approximately 50% during climate events, whereas that of high-diversity communities with 16–32 species was more resistant, changing by only approximately 25%. By a year after each climate event, ecosystem productivity had often fully recovered, or overshot, normal levels of productivity in both high- and low-diversity communities, leading to no detectable dependence of ecosystem resilience on biodiversity. Our results suggest that biodiversity mainly stabilizes ecosystem productivity, and productivity-dependent ecosystem services, by increasing resistance to climate events. Anthropogenic environmental changes that drive biodiversity loss thus seem likely to decrease ecosystem stability, and restoration of biodiversity to increase it, mainly by changing the resistance of ecosystem productivity to climate events.

Plant diversity effects on grassland productivity are robust to both nutrient enrichment and drought

Global change drivers are rapidly altering resource availability and biodiversity. While there is consensus that greater biodiversity increases the functioning of ecosystems, the extent to which biodiversity buffers ecosystem productivity in response to changes in resource availability remains unclear. We use data from 16 grassland experiments across North America and Europe that manipulated plant species richness and one of two essential resources—soil nutrients or water—to assess the direction and strength of the interaction between plant diversity and resource alteration on above-ground productivity and net biodiversity, complementarity, and selection effects. Despite strong increases in productivity with nutrient addition and decreases in productivity with drought, we found that resource alterations did not alter biodiversity–ecosystem functioning relationships. Our results suggest that these relationships are largely determined by increases in complementarity effects along plant species richness gradients. Although nutrient addition reduced complementarity effects at high diversity, this appears to be due to high biomass in monocultures under nutrient enrichment. Our results indicate that diversity and the complementarity of species are important regulators of grassland ecosystem productivity, regardless of changes in other drivers of ecosystem function.