SLEDGE: Sequential Labeling of Image Edges for Boundary Detection

Our goal is to detect boundaries of objects or surfaces occurring in an arbitrary image. We present a new approach that discovers boundaries by sequential labeling of a given set of image edges. A visited edge is labeled as on or off a boundary, based on the edge’s photometric and geometric properties, and evidence of its perceptual grouping with already identified boundaries. We use both local Gestalt cues (e.g., proximity and good continuation), and the global Helmholtz principle of non-accidental grouping. A new formulation of the Helmholtz principle is specified as the entropy of a layout of image edges. For boundary discovery, we formulate a new, policy iteration algorithm, called SLEDGE. Training of SLEDGE is iterative. In each training image, SLEDGE labels a sequence of edges, which induces loss with respect to the ground truth. These sequences are then used as training examples for learning SLEDGE in the next iteration, such that the total loss is minimized. For extracting image edges that are input to SLEDGE, we use our new, low-level detector. It finds salient pixel sequences that separate distinct textures within the image. On the benchmark Berkeley Segmentation Datasets 300 and 500, our approach proves robust and effective. We outperform the state of the art both in recall and precision for different input sets of image edges

Scene shape from texture of objects

Joint reasoning about objects and 3D scene layout has shown great promise in scene interpretation. One visual cue that has been overlooked is texture arising from a spatial repetition of objects in the scene (e.g., windows of a building). Such texture provides scene-specific constraints among objects, and thus facilitates scene interpretation. We present an approach to: (1) detecting distinct textures of objects in a scene, (2) reconstructing the 3D shape of detected texture surfaces, and (3) combining object detections and shape-from-texture toward a globally consistent scene interpretation. Inference is formulated within the reinforcement learning framework as a sequential interpretation of image regions, starting from confident regions to guide the interpretation of other regions. Our algorithm finds an optimal policy that maps states of detected objects and reconstructed surfaces to actions which ought to be taken in those states, including detecting new objects and identifying new textures, so as to minimize a long-term loss. Tests against ground truth obtained from stereo images demonstrate that we can coarsely reconstruct a 3D model of the scene from a single image, without learning the layout of common scene surfaces, as done in prior work. We also show that reasoning about texture of objects improves object detection. 1

Methodological Framework for Assessing Environmental Impacts of Photovoltaics Systems Using the Life Cycle Analysis Method

International audienceThe increasing demand for photovoltaics systems and the growing interests for environmental issues have encouraged the French government to bet ter consider the environmental aspects of the photovoltaic sector by int roducing Life Cycle Assessment results in call for tenders as a significant criterion. T he French Environment and Energy Management Agency (ADEME) has been asked to devise an environmental methodological framework for photovoltaic systems to help installers and designers of photovoltaics systems meet these new requirements. The methodological framework should be used for environmental assessments when making public policy decisions regarding the implementat ion of photovoltaic systems

Rapid Improvement after Starting Elexacaftor–Tezacaftor–Ivacaftor in Patients with Cystic Fibrosis and Advanced Pulmonary Disease

International audienceRationale: Elexacaftor-tezacaftor-ivacaftor is a CFTR (cystic fibrosis [CF] transmembrane conductance regulator) modulator combination, developed for patients with CF with at least one Phe508del mutation. Objectives: To evaluate the effects of elexacaftor-tezacaftor- ivacaftor in patients with CF and advanced respiratory disease. Methods: A prospective observational study, including all patients aged ⩾12 years and with a percent-predicted FEV1 (ppFEV1) <40 who initiated elexacaftor-tezacaftor-ivacaftor from December 2019 to August 2020 in France was conducted. Clinical characteristics were collected at initiation and at 1 and 3 months. Safety and effectiveness were evaluated by September 2020. National-level transplantation and mortality figures for 2020 were obtained from the French CF and transplant centers and registries. Measurements and Main Results: Elexacaftor-tezacaftor- ivacaftor was initiated in 245 patients with a median (interquartile range) ppFEV1 = 29 (24-34). The mean (95% confidence interval) absolute increase in the ppFEV1 was +15.1 (+13.8 to +16.4; P < 0.0001), and the mean (95% confidence interval) in weight was +4.2 kg (+3.9 to +4.6; P < 0.0001). The number of patients requiring long-term oxygen, noninvasive ventilation, and/or enteral tube feeding decreased by 50%, 30%, and 50%, respectively (P < 0.01). Although 16 patients were on the transplant waiting list and 37 were undergoing transplantation evaluation at treatment initiation, only 2 received a transplant, and 1 died. By September 2020, only five patients were still on the transplantation path. Compared with the previous 2 years, a twofold decrease in the number of lung transplantations in patients with CF was observed in 2020, whereas the number of deaths without transplantation remained stable. Conclusions: In patients with advanced disease, elexacaftor-tezacaftor-ivacaftor is associated with rapid clinical improvement, often leading to the indication for lung transplantation being suspended

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old