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Although various navigation apps are available, people who are blind or have low vision (PVIB) still face challenges to locate store entrances due to missing geospatial information in existing map services. Previously, we have developed a crowdsourcing platform to collect storefront accessibility and localization data to address the above challenges. In this paper, we have significantly improved the efficiency of data collection and user engagement in our new AI-enabled Smart DoorFront platform by designing and developing multiple important features, including a gamified credit ranking system, a volunteer contribution estimator, an AI-based pre-labeling function, and an image gallery feature. For achieving these, we integrate a specially designed deep learning model called MultiCLU into the Smart DoorFront. We also introduce an online machine learning mechanism to iteratively train the MultiCLU model, by using newly labeled storefront accessibility objects and their locations in images. Our new DoorFront platform not only significantly improves the efficiency of storefront accessibility data collection, but optimizes user experience. We have conducted interviews with six adults who are blind to better understand their daily travel challenges and their feedback indicated that the storefront accessibility data collected via the DoorFront platform would be very beneficial for them.

Mobile and embedded devices are becoming ubiquitous. Applications such as rescue with autonomous robots and event analysis on traffic cameras rely on devices with limited power supply and computational sources. Thus, the demand for efficient computer vision algorithms increases. Since 2015, we have organized the IEEE Low-Power Computer Vision Challenge to advance the state of the art in low-power computer vision. We describe the competition organizing details including the challenge design, the reference solution, the dataset, the referee system, and the evolution of the solutions from two winning teams. We examine the winning teams’ development patterns and design decisions, focusing on their techniques to balance power consumption and accuracy. We conclude that a successful competition needs a well-designed reference solution and automated referee system, and a solution with modularized components is more likely to win. We hope this paper provides guidelines for future organizers and contestants of computer vision competitions.

Accurate nuclear reaction rates for²⁶P(p,γ)²⁷S are pivotal for a comprehensive understanding of therp-process nucleosynthesis path in the region of proton-rich sulfur and phosphorus isotopes. However, large uncertainties still exist in the current rate of²⁶P(p,γ)²⁷S because of the lack of nuclear mass and energy level structure information for²⁷S. We reevaluate this reaction rate using the experimentally constrained²⁷S mass, together with the shell model predicted level structure. It is found that the²⁶P(p,γ)²⁷S reaction rate is dominated by a direct capture reaction mechanism despite the presence of three resonances atE= 1.104, 1.597, and 1.777 MeV above the proton threshold in²⁷S. The new rate is overall smaller than the other previous rates from the Hauser–Feshbach statistical model by at least 1 order of magnitude in the temperature range of X-ray burst interest. In addition, we consistently update the photodisintegration rate using the new²⁷S mass. The influence of new rates of forward and reverse reaction in the abundances of isotopes produced in therp-process is explored by postprocessing nucleosynthesis calculations. The final abundance ratio of²⁷S/²⁶P obtained using the new rates is only 10% of that from the old rate. The abundance flow calculations show that the reaction path²⁶P(p,γ)²⁷S(β⁺,ν)²⁷P is not as important as previously thoughtmore »for producing²⁷P. The adoption of the new reaction rates for²⁶P(p,γ)²⁷S only reduces the final production of aluminum by 7.1% and has no discernible impact on the yield of other elements.

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As visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user's view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world‐in‐miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web‐based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.

Predicting the secondary structure of RNA is an important problem in molecular biology, providing insights into the function of non-coding Rn As and with broad applications in understanding disease, the development of new drugs, among others. Combinatorial algorithms for predicting RNA foldings can generate an exponentially large number of equally optimal foldings with respect to a given optimization criterion, making it difficult to determine how well any single folding represents the entire space. We provide efficient new algorithms for providing insights into this large space of optimal RNA foldings and a research software tool, toRNAdo, that implements these algorithms.