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Plants exhibit plasticity in response to various external conditions, characterized by changes in physiological and morphological features. Although being non-negligible, compared to the other environmental factors, the effect of wind on plant growth is less extensively studied, either experimentally or computationally. This study aims to propose a modeling approach that can simulate the impact of wind on plant growth, which brings a biomechanical feedback to growth and biomass distribution into a functional–structural plant model (FSPM). Tree reaction to the wind is simulated based on the hypothesis that plants tend to fit in the environment best. This is interpreted as an optimization problem of finding the best growth-regulation sink parameter giving the maximal plant fitness (usually seed weight, but expressed as plant biomass and size). To test this hypothesis in silico, a functional–structural plant model, which simulates both the primary and secondary growth of stems, is coupled with a biomechanical model which computes forces, moments of forces, and breakage location in stems caused by both wind and self-weight increment during plant growth. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. The digital trees show considerable phenotypic plasticity under different wind speeds, whose behavior, as an emergent property, is in accordance with experimental results from works of literature: the height and leaf area of individual trees decreased with wind speed, and the diameter at the breast height (DBH) increased at low-speed wind but declined at higher-speed wind. Stronger wind results in a smaller tree. Such response of trees to the wind is realistically simulated, giving a deeper understanding of tree behavior. The result shows that the challenging task of modeling plant plasticity may be solved by optimizing the plant fitness function. Adding a biomechanical model enriches FSPMs and opens a wider application of plant models.

In this paper, we investigate how various types of subsidiary roles affect dual knowledge flows between a focal subsidiary and the multinational enterprise’s headquarters, a thus far overlooked topic in the knowledge management literature. We propose that subsidiaries with a world mandate have a stronger positive impact on dual knowledge flows than subsidiaries with a specialized contributor role. In contrast, we argue that subsidiaries with a local implementer role have a negative impact on dual knowledge flows. Further, we investigate the moderating effect of two different organizational governance types, namely, belonging to a South Korean business group (i.e., Chaebol), and being a small and medium-sized enterprise. Overall, our results from a sample of 1213 foreign manufacturing subsidiaries from 191 South Korean MNEs provide empirical evidence that validates our hypotheses.

To elucidate the mechanisms underlying the differences in yield formation among two parents (P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model (FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm (PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organ-level model may provide a computational approach to define the target of breeding by combination with a genetic model.

International audience; Finger-vein as a high security biometric characteristic has been widely investigated for verification. One of challenges in finger-vein recognition is the image-quality degradation as spurious and missing features in poor quality images can increase the verification error. Despite recent advances in finger-vein quality assessment, these solutions depend on domain knowledge. Training a deep Neural Network (DNN) based on the objective labels selected automatically has compromised this problem, but their performance is still limit because some quality attributes are ignored. In this work, we propose a DNN for representation learning from both grayscale and binary images to predict vein quality. In the proposed approach, the grayscale and binary images are directly input to DNN to learn the joint representations for quality assessment. Experimental results on one large public dataset demonstrates that the proposed method accurately identifies high and low quality images and outperforms other methods in terms of impact on equal error rate (EER) decrease

International audience; In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Principal Component Analysis (PCA) method is used to extract the features and to reduce the data dimension. Then, the Kullback-Leibler Divergence (KLD) is used to detect the fault occurrence by comparing the Probability Density Function of the latent scores. The faulty sensor is isolated thanks to a linear combination of the original measurements with binary coefficients denoted as Z-decomposition. The proposed approach is experimentally verified with vibration signals used for monitoring bearings in electrical machines.

International audience; Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy

Part 2: Semiotics of Interactions and Socially Aware User Interface Design; International audience; Social recommendation plays an important role in solving the cold start problem in recommendation systems and improves the accuracy of recommendation, but still faces serious challenges and problems. Ratings or relationships injected by fake users seriously affect the authenticity of the recommendations as well as users’ trustiness on the recommendation systems. Moreover, the simplification of relationship treatment also seriously affects the recommendation accuracy and user satisfaction to the recommendation systems. This paper first analyzes up to date research of social recommendation and the detecting technology of multiple relationships. Furthermore, it proposes a future research framework for robust social recommendations including modeling and feature extraction of multidimensional relationships, social recommendation shilling attack models based on social relationships, the analysis of the relationships in social networks as well as the roles of relationships on recommendation, and robust social recommendation approaches taking multiple relationships into consideration.