Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7

Cellular responses of Candida albicans to phagocytosis and the extracellular activities of neutrophils are critical to counteract carbohydrate starvation, oxidative and nitrosative stress

Acknowledgments We thank Alexander Johnson (yhb1D/D), Karl Kuchler (sodD/D mutants), Janet Quinn (hog1D/D, hog1/cap1D/D, trx1D/D) and Peter Staib (ssu1D/D) for providing mutant strains. We acknowledge helpful discussions with our colleagues from the Microbial Pathogenicity Mechanisms Department, Fungal Septomics and the Microbial Biochemistry and Physiology Research Group at the Hans Kno¨ll Institute (HKI), specially Ilse D. Jacobsen, Duncan Wilson, Sascha Brunke, Lydia Kasper, Franziska Gerwien, Sea´na Duggan, Katrin Haupt, Kerstin Hu¨nniger, and Matthias Brock, as well as from our partners in the FINSysB Network. Author Contributions Conceived and designed the experiments: PM HW IMB AJPB OK BH. Performed the experiments: PM CD HW. Analyzed the data: PM HW IMB AJPB OK BH. Wrote the paper: PM HW OK AJPB BH.Peer reviewedPublisher PD

Observed NO/NO2 Ratios in the Upper Troposphere Imply Errors in NO-NO2-O3 Cycling Kinetics or an Unaccounted NOx Reservoir

Observations from the SEAC4RS aircraft campaign over the southeast United States in August-September 2013 show NO/NO2 concentration ratios in the upper troposphere that are approximately half of photochemical equilibrium values computed from Jet Propulsion Laboratory (JPL) kinetic data. One possible explanation is the presence of labile NOx reservoir species, presumably organic, decomposing thermally to NO2 in the instrument. The NO2 instrument corrects for this artifact from known labile HNO4 and CH3O2NO2 NOx reservoirs. To bridge the gap between measured and simulated NO2, additional unaccounted labile NOx reservoir species would have to be present at a mean concentration of ~40 ppt for the SEAC4RS conditions (compared with 197 ppt for NOx). An alternative explanation is error in the low-temperature rate constant for the NO + O3 reaction (30% 1-σ uncertainty in JPL at 240 K) and/or in the spectroscopic data for NO2 photolysis (20% 1-σ uncertainty). Resolving this discrepancy is important for understanding global budgets of tropospheric oxidants and for interpreting satellite observations of tropospheric NO2 columns

Recent Trends and Perspectives on Defect-Oriented Testing

Electronics employed in modern safety-critical systems require severe qualification during the manufacturing process and in the field, to prevent fault effects from manifesting themselves as critical failures during mission operations. Traditional fault models are not sufficient anymore to guarantee the required quality levels for chips utilized in mission-critical applications. The research community and industry have been investigating new test approaches such as device-aware test, cell-aware test, path-delay test, and even test methodologies based on the analysis of manufacturing data to move the scope from OPPM to OPPB. This special session presents four contributions, from academic researchers and industry professionals, to enable better chip quality. We present results on various activities towards this objective, including device-aware test, software-based self-test, and memory test