Developing radical innovation capabilities: Exploring the effects of training employees for creativity and innovation

The resilience of organizations is increasingly dependent on their ability to develop radical innovation capabilities. While the literature documents numerous cases of organizations that already have radical innovation capabilities, the question of organizational devices that can be used to stimulate the emergence of such capabilities remains poorly addressed. Specifically, training for innovation and creativity has been proposed as a means to foster innovation capabilities; however, there has been little empirical evidence concerning the long‐term impacts of such training. To fill this gap, this article aims to document and evaluate the efforts of the research institute of a major Canadian energy company to provide training for innovation and creativity to initiate a radical innovation capability. We rely on a longitudinal study over the span of 18 months, where we observed 128 h of training and conducted 70 semi‐structured interviews with a sample of 40 researchers. We found that training for creativity and innovation has the potential to develop individual creative skills for exploration, to catalyze and federate collective action through common methods and a shared sense of what innovation entails, and to help create a common language and vocabulary between the different groups or divisions of an organization to talk about exploration.     

Ozone Effects on Botrytis cinerea Conidia using a Bubble Column: Germination Inactivation and Membrane Phospholipids Oxidation

Ozone treatments were applied on conidia aqueous suspensions in order to determine the minimal applied ozone dose to limit conidia germination and to observe the mechanisms involved in the spores inactivation. Conidia germination was significantly reduced, bubbling for at least 0.5 min as a gas with a minimal ozone concentration of 1 g.m^?3. The applied ozone doses induce the membrane phospholipids oxidation, determined by the malondialdehyde quantification. Membrane phospholipids oxidation and inactivation rate are correlated. So, lipid peroxidation and consequently the alteration of the membrane integrity are involved in the antifungal action of ozone.     

Vectorized algorithms for spiking neural network simulation

High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.     

Comparing dynamics of fluency and inter-limb coordination in climbing activities using multi-scale Jensen–Shannon embedding and clustering

This paper reports the results of two studies carried out in a controlled environment aiming to understand relationships between movement patterns of coordination that emerge during climbing and performance outcomes. It involves a recent method of nonlinear dimensionality reduction, multi-scale Jensen–Shannon neighbor embedding (Lee et al., 2015), which has been applied to recordings of movement sensors in order to visualize coordination patterns adapted by climbers. Initial clustering at the climb scale provides details linking behavioral patterns with climbing fluency/smoothness (i.e., the performance outcome). Further clustering on shorter time intervals, where individual actions within a climb are analyzed, enables more detailed exploratory data analysis of behavior. Results suggest that the nature of individual learning curves (the global, trial-to-trial performance) corresponded to certain behavioral patterns (the within trial motor behavior). We highlight and discuss three distinctive learning curves and their corresponding relationship to behavioral pattern emergence, namely: no improvement and a lack of new motor behavior emergence; sudden improvement and the emergence of new motor behaviors; and gradual improvement and a lack of new motor behavior emergence.     

Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user’s musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.

     

A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition

In this paper, music genre taxonomies are used to design hierarchical classifiers that perform better than flat classifiers. More precisely, a novel method based on sequential pattern mining techniques is proposed for the extraction of relevant characteristics that enable to propose a vector representation of music genres. From this representation, the agglomerative hierarchical clustering algorithm is used to produce music genre taxonomies. Experiments are realized on the GTZAN dataset for performances evaluation. A second evaluation on GTZAN augmented by Afro genres has been made. The results show that the hierarchical classifiers obtained with the proposed taxonomies reach accuracies of 91.6 % (more than 7 % higher than the performances of the existing hierarchical classifiers).     

A Vectorial Framework for Ray Traced Diffusion Curves

Diffusion curves allow creating complex, smoothly shaded images by diffusing colours defined at curves. These methods typically require the solution of a global optimization problem (over either the pixel grid or an intermediate tessellated representation) to produce the final image, making fully parallel implementation challenging. An alternative approach, inspired by global illumination, uses 2D ray tracing to independently compute each pixel value. This formulation allows trivial parallelism, but it densely computes values even in smooth regions and sacrifices support for instancing and layering. We describe a sparse, ray traced, multi‐layer framework that incorporates many complementary benefits of these existing approaches. Our solution avoids the need for a global solve and trivially allows parallel GPU implementation. We leverage an intermediate triangular representation with cubic patches to synthesize smooth images faithful to the per‐pixel solution. The triangle mesh provides a resolution–independent, vectorial representation and naturally maps diffusion curve images to a form natively supported by standard vector graphics and triangle rasterization pipelines. Our approach supports many features which were previously difficult to incorporate into a single system, including instancing, layering, alpha blending, texturing, local blurring, continuity control and parallel computation. We also show how global diffusion curves can be combined with local painted strokes in one coherent system.     

Improving shape depiction under arbitrary rendering

Based on the observation that shading conveys shape information through intensity gradients, we present a new technique called Radiance Scaling that modifies the classical shading equations to offer versatile shape depiction functionalities. It works by scaling reflected light intensities depending on both surface curvature and material characteristics. As a result, diffuse shading or highlight variations become correlated with surface feature variations, enhancing concavities and convexities. The first advantage of such an approach is that it produces satisfying results with any kind of material for direct and global illumination: we demonstrate results obtained with Phong and Ashikmin-Shirley BRDFs, Cartoon shading, sub-Lambertian materials, perfectly reflective or refractive objects. Another advantage is that there is no restriction to the choice of lighting environment: it works with a single light, area lights, and interreflections. Third, it may be adapted to enhance surface shape through the use of precomputed radiance data such as Ambient Occlusion, Prefiltered Environment Maps or Lit Spheres. Finally, our approach works in real time on modern graphics hardware making it suitable for any interactive 3D visualization.