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Detecting text in natural images is an important prerequisite. In this paper, we propose a novel text detection algorithm, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates. These candidates are then filtered using geometric and stroke width information to exclude non-text objects. Letters are paired to identify text lines, which are subsequently separated into words. We evaluate our system using the ICDAR competition dataset and our mobile document database. The experimental results demonstrate the excellent performance of the proposed method.

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Global municipal waste production causes multiple environmental impacts, including greenhouse gas emissions, ocean plastic accumulation, and nitrogen pollution. However, estimates of both past and future development of waste and pollution are scarce. We apply compositional Bayesian regression to produce the first estimates of past and future (1965–2100) waste generation disaggregated by composition and treatment, along with resultant environmental impacts, for every country. We find that total wastes grow at declining speed with economic development, and that global waste generation has increased from 635 Mt in 1965 to 1999 Mt in 2015 and reaches 3539 Mt by 2050 (median values, middle-of-the-road scenario). From 2015 to 2050, the global share of organic waste declines from 47% to 39%, while all other waste type shares increase, especially paper. The share of waste treated in dumps declines from 28% to 18%, and more sustainable recycling, composting, and energy recovery treatments increase. Despite these increases, we estimate environmental loads to continue increasing in the future, although yearly plastic waste input into the oceans has reached a peak. Waste production does not appear to follow the environmental Kuznets curve, and current projections do not meet UN SDGs for waste reduction. Our study shows that a continuation of current trends and improvements is insufficient to reduce pressures on natural systems and achieve a circular economy. Relative to 2015, the amount of recycled waste would need to increase from 363 Mt to 740 Mt by 2030 to begin reducing unsustainable waste generation, compared to 519 Mt currently projected.

Abstract. The open-source modeling framework MAgPIE (Model of Agricultural Production and its Impact on the Environment) combines economic and biophysical approaches to simulate spatially explicit global scenarios of land use within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computationally intensive and non-transparent, requiring structured approaches to improve the development and evaluation of the model.Here, we provide an overview on version 4 of MAgPIE and how it addresses these issues of increasing complexity using new technical features: modular structure with exchangeable module implementations, flexible spatial resolution, in-code documentation, automatized code checking, model/output evaluation and open accessibility. Application examples provide insights into model evaluation, modular flexibility and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables.With the open-source release of the MAgPIE 4 framework, we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility, it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus.

Establishing visual correspondences is an essential component of many computer vision problems, which is often done with local feature-descriptors. Transmission and storage of these descriptors are of critical importance in the context of mobile visual search applications. We propose a framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate compared to state-of-theart descriptors. The framework offers low complexity and has significant speed-up in the matching stage. We show how to efficiently compute distances between descriptors in the compressed domain eliminating the need for decoding. We perform a comprehensive performance comparison with SIFT, SURF, BRIEF, MPEG-7 image signatures and other low bit-rate descriptors and show that our proposed CHoG descriptor outperforms existing schemes significantly over a wide range of bitrates. We implement the descriptor in a mobile image retrieval system and for a database of 1 million CD, DVD and book covers, we achieve 96% retrieval accuracy using only 4 KB of data per query image.

Abstract

Human immunodeficiency virus (HIV) positive individuals who adhere to their antiretroviral (ARV) regimens are more likely to achieve suppressed HIV viral load and improved immunologic response; however, for most patients, medication adherence remains a challenge. Prior studies have shown that clinical pharmacists contribute to the management of HIV-infected patients; but due to variability in clinical responsibilities and study limitations, their value has not been fully realized. The objective of this study was to investigate clinical outcomes of an HIV clinical pharmacist's interventions at Kaiser Permanente Medical Care Program, who utilizes medication expertise to provide recommendations for ARV regimen changes. The pharmacist suggests new ARV regimens in order to attain virologic suppression, improve immunologic response, or minimize ARV adverse effects, while aiming to optimize patients' adherence by decreasing pill burden and/or dosing frequency. This retrospective study assessed the effectiveness of the pharmacist's interventions that occurred between 11 September 2006 and 30 September 2008 on pill burden, dosing frequency, and medication adherence. Additionally, CD4+ cell count and HIV viral load pre- and post-intervention were evaluated. Medication adherence was assessed utilizing electronic pharmacy refill records and calculated based on the formula: [(pills dispensed/pills prescribed per day)/days between refills] x 100. From a cohort of 75 patients, mean daily pill quantity and dosing frequency decreased from 7.2 pills/day and 2.0 times/day in the control phase to 5.4 pills/day and 1.5 times/day in the study phase, respectively ( p < 0.001). Medication adherence increased from a mean of 81% in the control phase to 89% in the study phase ( p = 0.003). Clinical outcomes measured by CD4+cell count and CD4% were statistically improved and more individuals achieved undetectable HIV viral loads postintervention ( p < 0.001). In conclusion, HIV clinical pharmacists may play an important role in reducing pill burden and dosing frequency, increasing medication adherence, and improving clinical outcomes.