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20 % de plus, 20 % moins cher ». Le plan 3A de la Métropole de Lyon ou les ambiguïtés de l’accession abordable
Suite à la très forte hausse des prix immobiliers en France depuis 2000, certains territoires connaissent une crise de l’abordabilité du logement. C’est notamment le cas de la métropole lyonnaise, dont les prix immobiliers réels ont été multipliés par 3 en 20 ans. Face à cette situation, les collectivités territoriales ont la possibilité de développer des politiques d’accession sociale à la propriété. Ce fut le cas entre 2013 et 2020 sur le territoire du Grand Lyon via le Plan 3A (Accession À prix Abordable). Cette politique a permis à plus de 2 000 ménages d’acquérir un logement 20 % moins cher que les prix du marché et de bénéficier d’une prime à l’achat. Cette politique sociale de solvabilisation, plutôt consensuelle, est néanmoins ambiguë pour deux raisons : non coercitive, elle est d’une part un soutien à la production immobilière neuve, qui instrumentalise en partie les ménages acquéreurs, et d’autre part, adossée à des mécanismes de marché, elle ne place pas la collectivité en capacité d’agir profondément ni sur les prix fonciers-immobiliers ni sur la segmentation socio-résidentielle. Finalement, le Plan 3A questionne les marges de manœuvre de l’accession abordable sous la contrainte du marché immobilier.
Journal Article
Blue ocean shift : beyond competing : proven steps to inspire confidence and seize new growth
Drawing on more than a decade of new work, the authors demonstrate how to move beyond competing, inspire confidence, and seize new growth, discussing how to take an organization into an uncontested market space.
Book
TMD-Unet: Triple-Unet with Multi-Scale Input Features and Dense Skip Connection for Medical Image Segmentation
Deep learning is one of the most effective approaches to medical image processing applications. Network models are being studied more and more for medical image segmentation challenges. The encoder-decoder structure is achieving great success, in particular the Unet architecture, which is used as a baseline architecture for the medical image segmentation networks. Traditional Unet and Unet-based networks still have a limitation that is not able to fully exploit the output features of the convolutional units in the node. In this study, we proposed a new network model named TMD-Unet, which had three main enhancements in comparison with Unet: (1) modifying the interconnection of the network node, (2) using dilated convolution instead of the standard convolution, and (3) integrating the multi-scale input features on the input side of the model and applying a dense skip connection instead of a regular skip connection. Our experiments were performed on seven datasets, including many different medical image modalities such as colonoscopy, electron microscopy (EM), dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). The segmentation applications implemented in the paper include EM, nuclei, polyp, skin lesion, left atrium, spleen, and liver segmentation. The dice score of our proposed models achieved 96.43% for liver segmentation, 95.51% for spleen segmentation, 92.65% for polyp segmentation, 94.11% for EM segmentation, 92.49% for nuclei segmentation, 91.81% for left atrium segmentation, and 87.27% for skin lesion segmentation. The experimental results showed that the proposed model was superior to the popular models for all seven applications, which demonstrates the high generality of the proposed model.
Journal Article
A deep learning-based algorithm for 2-D cell segmentation in microscopy images
Automatic and reliable characterization of cells in cell cultures is key to several applications such as cancer research and drug discovery. Given the recent advances in light microscopy and the need for accurate and high-throughput analysis of cells, automated algorithms have been developed for segmenting and analyzing the cells in microscopy images. Nevertheless, accurate, generic and robust whole-cell segmentation is still a persisting need to precisely quantify its morphological properties, phenotypes and sub-cellular dynamics.
We present a single-channel whole cell segmentation algorithm. We use markers that stain the whole cell, but with less staining in the nucleus, and without using a separate nuclear stain. We show the utility of our approach in microscopy images of cell cultures in a wide variety of conditions. Our algorithm uses a deep learning approach to learn and predict locations of the cells and their nuclei, and combines that with thresholding and watershed-based segmentation. We trained and validated our approach using different sets of images, containing cells stained with various markers and imaged at different magnifications. Our approach achieved a 86% similarity to ground truth segmentation when identifying and separating cells.
The proposed algorithm is able to automatically segment cells from single channel images using a variety of markers and magnifications.
Journal Article
EfficientPS: Efficient Panoptic Segmentation
Understanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.
Journal Article
Blue ocean classics
\"W. Chan Kim and Renâee Mauborgne changed the field of strategy and the language of business forever with their path-breaking \"blue ocean strategy,\" a new model for creating uncontested markets that unlock all new demand and growth. This book brings together three of their classic Harvard Business Review articles that upend traditional thinking for creating and capturing lasting value. In Blue Ocean Strategy, Kim and Mauborgne introduce the idea of blue oceans, previously unknown market spaces where demand is created rather than fought over, and the opportunities for profitable growth are wide and untainted. The article highlights the distinctive differences between market-competing and market-creating strategy and what it takes to create the new markets of tomorrow. Red Ocean Traps shows that managers' mental models--ingrained assumptions about the way the world works--undermine attempts to make market-creating strategic moves that unlock blue oceans. Kim and Mauborgne provide a framework that reveals the assumptions managers make in creating new markets that keep their efforts tethered to existing overcrowded industries (red oceans). In Blue Ocean Leadership, the authors address what is perhaps the greatest challenge of leadership: closing the gulf between the potential and the realized talent and energy of employees. They outline an approach to uncovering which leadership acts and activities will inspire employees, and a process for getting managers to start doing them\"-- Provided by publisher.
Book
Status Signals
Why are elite jewelers reluctant to sell turquoise, despite strong demand? Why did leading investment bankers shun junk bonds for years, despite potential profits?Status Signalsis the first major sociological examination of how concerns about status affect market competition. Starting from the basic premise that status pervades the ties producers form in the marketplace, Joel Podolny shows how anxieties about status influence whom a producer does (or does not) accept as a partner, the price a producer can charge, the ease with which a producer enters a market, how the producer's inventions are received, and, ultimately, the market segments the producer can (and should) enter. To achieve desired status, firms must offer more than strong past performance and product quality--they must also send out and manage social and cultural signals.
Through detailed analyses of market competition across a broad array of industries--including investment banking, wine, semiconductors, shipping, and venture capital--Podolny demonstrates the pervasive impact of status. Along the way, he shows how corporate strategists, tempted by the profits of a market that would negatively affect their status, consider not only whether to enter the market but also whether they can alter the public's perception of the market. Podolny also examines the different ways in which a firm can have status. Wal-Mart, for example, has low status among the rich as a place to shop, but high status among the rich as a place to invest.
Status Signalsprovides a systematic understanding of market dynamics that have--until now--not been fully appreciated.
eBook