An Analysis of the Sustainability of the Increasing Consumption of Bolivian and Peruvian Quinoa at University Canteens in Berlin

The THESys Discussion Paper “An Analysis of the Sustainability of the Increasing Consumption of Bolivian and Peruvian Quinoa at University Canteens in Berlin” represents the first report in this series compiled solely by bachelor’s and master’s students. It therefore adds an important new category to the series, one that provides a platform for innovative interdisciplinary research conducted by students. The authors are students at Humboldt-Universität’s Departments of European Ethnology, Geography, Philosophy and Physics, the Thaer-Institute of Agricultural and Horticultural Sciences as well as the School of Economics. They all are or have been members of the so-called Themenklasse Nachhaltigkeit & Globale Gerechtigkeit, (Themenklasse Sustainability & Global Justice), a year-long interdisciplinary study project at IRI THESys for fifteen students who receive a monthly scholarship from the German federal government’s Deutschlandstipendium programme. The scholarships, which reward academic excellence and social engagement, are provided by the Stiftung Humboldt-Universität, with co-funding from the Federal Ministry for Education and Research. The Themenklasse Nachhaltigkeit & Globale Gerechtigkeit has existed since 2013. Since its inception, the students of the Themenklasse have used their one year scholarship period to carry out interdisciplinary group work on questions of sustainability and global justice, under the supervision of IRI THESys scientists. In this work, which has always fallen under the larger topic of “Humboldt’s Footprint”, the students have addressed questions of great societal relevance while using the “cosmos” of their university as an area or object of study. Their work has included projects on subjects such as the sustainability of the Humboldt- Universität’s supply chains, student mobility, and official travel at the university’s geography department. The 2016/2017 cohort also decided to focus on Humboldt’s Footprint, this time addressing the question of sustainable food production and consumption. The students began by exploring and comparing different disciplinary approaches to the question of sustainability in a resource context. After determining the major differences in disciplinary approaches and perspectives, they then narrowed down the often broader, more general questions to the specific question of Quinoa consumption in university canteens. During many long meetings and discussions, and with only brief inputs from their supervisors, the students explored the multi-faceted problem of how to assess Quinoa as a product, including its production, transport and consumption. They took approaches to this question of sustainable quinoa consumption that ranged from empirical quantitative work to a normative approach. This report presents an initial summary and synthesis of the outcomes of this work. It is not a final report, as the work of the 2017/2018 cohort will continue to examine this topic. In a June 2017 workshop, the group presented their work to fellow students and explored how this topic could be further refined and developed, e.g. to regionally differentiate the economic and social impacts of the diffusion of quinoa production. As the supervisors of this work, we are excited to learn about the next group of new ideas and to see the outcomes of the next steps in this analysis. We therefore want to express our gratitude to Stiftung Humboldt-Universität for their constant support, which has been essential to ensuring the continuity of the work of this group of talented and enthusiastic young researchers

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac