Managed Forgetting to Support Information Management and Knowledge Work

Trends like digital transformation even intensify the already overwhelming mass of information knowledge workers face in their daily life. To counter this, we have been investigating knowledge work and information management support measures inspired by human forgetting. In this paper, we give an overview of solutions we have found during the last five years as well as challenges that still need to be tackled. Additionally, we share experiences gained with the prototype of a first forgetful information system used 24/7 in our daily work for the last three years. We also address the untapped potential of more explicated user context as well as features inspired by Memory Inhibition, which is our current focus of research.Comment: 10 pages, 2 figures, preprint, final version to appear in KI - K\"unstliche Intelligenz, Special Issue: Intentional Forgettin

Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms\u27 survival. Predictions of customers\u27 Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks

MODELIZACIÓN EMPÍRICA DEL ÍNDICE DE ÁREA FOLIAR EN ECOSISTEMAS DE DEHESA: INTEGRACIÓN DE DATOS DE CAMPO, AEROPORTADOS Y DE SATÉLITE

El índice de área foliar es considerado un bioindicador del estado de la salud real de las plantas y de la productividad primaria bruta de la vegetación. Numerosos estudios han demostrado que los modelos basados ee regresión simple lineal son herramientas óptimas que tienen la capacidad de relacionar el LAI medido en campo con información derivada de teledetección óptica, El objetivo del presente Trabajo Fin de Máster es desarrollar un modelo predictivo de LAI a partir de informa-ción multiespectral de media resolución espacial (Landsat) a partir del análisis y modelización pre-via de las relaciones entre información hiperespectral a alta resolución espacial y LAI verdad-te-rreno utilizando la técnica upcaling y, desarrollado para ambientes heterogéneos como son las dehesas. Para ello, se han utilizado datos hiperespectrales derivados del sensor CASI y datos del LAI medida en campo proporcionados por SynerTGE y una gama de índices de Vegetación derivados de los productos Landsat TM y OLI. Un primer análisis se basó en establecer relaciones empíricas entre pseudo-LAI e índices de vegetación. Para seguir evaluando el rendimiento del modelo, se aplicaron análisis de regresión (RLS) para modelizar la relación entre pseudo-LAI e índices de ve-getación. Los resultados establecieron que el método propuesto varía en función de los modelos utilizados. Por otra parte, se desarrolló un modelo para i) aplicar y modelizar las funciones predic-tivas generadas mediante los análisis RLS y, ii) validar los productos mediante estadístico RMSE. Para ello, se utilizaron series multitemporales derivadas de Landast-8 OLI y muestras de LAI total y LAI verde repartidas en 5 jornadas de campo, en cada parcela (11), las muestras fueron tomadas sobre 3 cuadrantes (25x25cm), además, las muestras tomadas se consideran, a priori, represen-tativas a distintos momentos de la dinámica fenológica. Los resultados obtenidos establecen que los modelos predictivos rinden mejor para periodos primaverales-estivales, cuando el pastizal se encuentra en su periodo de máximo crecimiento. Además, el modelo desarrollado sobre pasto y encinares rinde mejor que el modelo A. Si individualizamos los casos, se establece que el modelo predictivo en fecha del 28 de junio de 2015 obtuvo los mejores valores RMSE = 0.196 y RMSE (%) = 6.73 para predecir la variable biofísica LAI verde

Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania

More than ever, it is crucial to make the best use of existing country data, and analytical tools for developing malaria control strategies as the heterogeneity in malaria risk within countries is increasing, and the available malaria control tools are expanding while large funding gaps exist. Global and local policymakers, as well as funders, increasingly recognize the value of mathematical modelling as a strategic tool to support decision making. This case study article describes the long-term use of modelling in close collaboration with the National Malaria Control Programme (NMCP) in Tanzania, the challenges encountered and lessons learned.; In Tanzania, a recent rebound in prevalence led to the revision of the national malaria strategic plan with interventions targeted to the malaria risk at the sub-regional level. As part of the revision, a mathematical malaria modelling framework for setting specific predictions was developed and used between 2016 and 2019 to (1) reproduce setting specific historical malaria trends, and (2) to simulate in silico the impact of future interventions. Throughout the project, multiple stakeholder workshops were attended and the use of mathematical modelling interactively discussed.; In Tanzania, the model application created an interdisciplinary and multisectoral dialogue platform between modellers, NMCP and partners and contributed to the revision of the national malaria strategic plan by simulating strategies suggested by the NMCP. The uptake of the modelling outputs and sustained interest by the NMCP were critically associated with following factors: (1) effective sensitization to the NMCP, (2) regular and intense communication, (3) invitation for the modellers to participate in the strategic plan process, and (4) model application tailored to the local context.; Empirical data analysis and its use for strategic thinking remain the cornerstone for evidence-based decision-making. Mathematical impact modelling can support the process both by unifying all stakeholders in one strategic process and by adding new key evidence required for optimized decision-making. However, without a long-standing partnership, it will be much more challenging to sensibilize programmes to the usefulness and sustained use of modelling and local resources within the programme or collaborating research institutions need to be mobilized

Urban Revitalization through Art, Community, and Ecology: The Heidelberg Project

Once known as the Motor City, Detroit is now rusted over with 90,000 vacant parcels and a 22% unemployment rate. Decline of manufacturing jobs, combined with a complex history of racism and discrimination, led to unprecedented population collapse and abandonment. The 2010 census revealed the dramatic exodus from Detroit was even greater than predicted: just over 713,000 residents remain, down from nearly 2 million in 1950. Leftover stretches of vacant land, totaling more than 40 square miles, pose an enormous physical and psychological challenge to residents and city officials forced to manage with what remains. Despite the challenges of vacant land, disenfranchisement, and economic hardship, many still see beauty in what’s left of the city. Twenty-five years ago, Detroit-native Tyree Guyton created the Heidelberg Project, a two-block long environmental artscape on the city’s eastside. The artwork became a beacon for his neighborhood and others like it, defiantly resisting the destruction wrought by neglect and disinvestment. The research and design presented in this document expands the scope of the Heidelberg Project into a long-term vision for neighborhood redevelopment called the Heidelberg Cultural Village. This project lays the groundwork for the Cultural Village, a model for art-based neighborhood redevelopment in Detroit and other post-industrial cities. The work is presented in four chapters: Christian Runge examines how the Heidelberg Cultural Village can be integrated with emerging ecological and cultural land uses specific to a post urban Detroit. Fai Foen’s work focuses on an alternative economic model that invests in the local economy and builds on existing human capacity to support sustainable redevelopment in Rustbelt communities. Sarah Alward explores how an art-based urban farm can allow for a diverse range of contributions from community members, creating an inclusive space to grow fresh, healthy food that has the potential to increase neighborhood investment and involvement. Finally, Dana Petit illustrates how a healing garden can respond to the social, psychological, and physical health issues stemming from the McDougall-Hunt neighborhood’s experience with abandonment and poverty. Together, these design interventions are intended to serve as an incubator for physical, economic, and cultural sustainability and the center of community life for the McDougall-Hunt neighborhood.Master of Landscape ArchitectureNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/84207/1/Heidelberg Opus 2011.pd

Highly efficient passive Tesla valves for microfluidic applications

A multistage optimization method is developed yielding Tesla valves that are efficient even at low flow rates, characteristic, e.g., for almost all microfluidic systems, where passive valves have intrinsic advantages over active ones. We report on optimized structures that show a diodicity of up to 1.8 already at flow rates of 20 μl s−1 corresponding to a Reynolds number of 36. Centerpiece of the design is a topological optimization based on the finite element method. It is set-up to yield easy-to-fabricate valve structures with a small footprint that can be directly used in microfluidic systems. Our numerical two-dimensional optimization takes into account the finite height of the channel approximately by means of a so-called shallow-channel approximation. Based on the three-dimensionally extruded optimized designs, various test structures were fabricated using standard, widely available microsystem manufacturing techniques. The manufacturing process is described in detail since it can be used for the production of similar cost-effective microfluidic systems. For the experimentally fabricated chips, the efficiency of the different valve designs, i.e., the diodicity defined as the ratio of the measured pressure drops in backward and forward flow directions, respectively, is measured and compared to theoretical predictions obtained from full 3D calculations of the Tesla valves. Good agreement is found. In addition to the direct measurement of the diodicities, the flow profiles in the fabricated test structures are determined using a two-dimensional microscopic particle image velocimetry (μPIV) method. Again, a reasonable good agreement of the measured flow profiles with simulated predictions is observed