Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure

Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures

The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper provides a dynamic optimization approach for enhanced cloud infrastructures, based on the concept of cloudlets, which are located at hotspot areas throughout a metropolitan area. In conjunction, we consider classical remote data centers that are rigid with respect to QoS but provide nearly abundant computation resources. Given fluctuating user demands, we optimize the cloudlet placement over a finite time horizon from a cloud infrastructure provider's perspective. By the means of a custom tailed heuristic approach, we are able to reduce the computational effort compared to the exact approach by at least three orders of magnitude, while maintaining a high solution quality with a moderate cost increase of 5.8% or less

Numerical Optimization of an Open-Ended Coaxial Slot Applicator for the Detection and Microwave Ablation of Tumors

A multiobjective optimization method for a dual-mode microwave applicator is proposed. Dual-modality means that microwaves are used apart from the treatment, and also for the monitoring of the microwave ablation intervention. (1) The use of computational models to develop and improve microwave ablation applicator geometries is essential for further advances in this field. (2) Numerical electromagnetic–thermal coupled simulation models are used to analyze the performance of the dual-mode applicator in liver tissue; the sensitivity evaluation of the dual-mode applicator’s sensing mode constrains the set of optimal solutions. (3) Three Pareto-optimal design parameter sets are derived that are optimal in terms of applicator efficiency as well as volume and sphericity of the ablation zone. The resulting designs of the dual-mode applicator provide a suitable sensitivity to distinguish between healthy and tumorous liver tissue. (4) The optimized designs are presented and numerically characterized. An improvement on the performance of previously proposed dual-mode applicator designs is achieved. The multiphysical simulation model of electromagnetic and thermal properties of the applicator is applicable for future comprehensive design procedures

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the ‘‘Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Altimetry for the future: building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Context-Aware Decision Making in Wireless Networks: Optimization and Machine Learning Approaches

In future wireless networks, an enormous number of heterogeneous devices will be connected, leading to a dramatic increase in data traffic. At the same time, future applications will have significantly higher requirements with respect to data rates, reliability, and latency. Conventional approaches, which aim at only improving the communication capabilities of wireless networks, will not be sufficient to satisfy the more demanding requirements arising in future. Hence, a paradigm shift is needed. While conventionally perceived as pure communication networks, wireless networks can provide not only communication resources, but also computation, caching, data collection, and even user resources. Such resources can be part of the network infrastructure and of the wirelessly connected devices and their users. This radically different view on wireless networks as networks of distributed connected resources calls for the development of new techniques that jointly consider and leverage different types of resources in order to improve the system performance. In this thesis, we show that such new techniques that jointly consider and leverage different types of resources require context-aware decision making. This is due to the fact that first, resources need to be shared and secondly, trade-offs between different types of resources exist. Thirdly, the optimal resource allocation may depend not only on network conditions, but also on other node-related, user-related or externally given conditions, the so-called context. We provide an overview of context-aware decision making by discussing context awareness, architectures of decision making, and designs of decision agents. Designing a context-aware decision-making framework requires to formulate a context-aware system model. In particular, decision agents responsible for resource allocation need to be identified. These agents may be part of a centralized, decentralized or hierarchical architecture of decision making and a suitable architecture needs to be selected. Finally, designing decision agents requires to model and classify the problem to be solved and to develop an appropriate method according to which decision agents take decisions. We emphasize two designs relevant for context-aware decision making in wireless networks, namely, optimization-based approaches and machine-learning-based approaches, in the latter case specifically the framework of multi-armed bandits. Moreover, in this thesis, we study three candidate techniques for wireless networks that jointly consider and leverage different types of resources, namely, computation offloading in multi-hop wireless networks, caching at the edge of wireless networks, and mobile crowdsourcing. For each technique, we identify a fundamental problem requiring context-aware decision making, propose a novel framework for context-aware decision making, and solve the problem using the proposed framework. Computation offloading allows wirelessly connected devices to offload computation tasks to resource-rich servers. This may reduce the devices' task completion times and their energy consumption. Computation offloading hence trades computation resources off against communication resources. In this thesis, for the first time, we study computation offloading in multi-hop wireless networks, where wirelessly connected devices assist each other as relay nodes. We identify the fundamental problem of context-aware computation offloading for energy minimization in multi-hop wireless networks. We propose a novel model that takes into account channel conditions, computing capabilities of the devices, task characteristics, and battery constraints at relay nodes since the effect of computation offloading on the devices' energy consumption depends on these context factors. Based on this model, we take an optimization-based approach and formulate the considered problem as a multi-dimensional knapsack problem, which takes into account that offloading decisions in multi-hop networks are non-trivially coupled as communication resources of relay nodes need to be shared. Finally, we propose a novel context-aware greedy heuristic algorithm for computation offloading in multi-hop networks. Based on its centralized architecture of decision making, this algorithm enables a central entity to take offloading decisions using centrally collected context information. We show that despite its centralized architecture, the algorithm has a small communication overhead. Numerical results demonstrate that the offloading solution found by the proposed algorithm on average reduces the network energy consumption by 13% compared to the case when no computation offloading is used. Moreover, the proposed algorithm yields near-optimal results in the considered offloading scenarios, with a maximal deviation of less than 6% from the global optimum. Caching at the edge allows popular content to be cached close to mobile users in order to serve user requests locally, thus reducing backhaul and cellular traffic as well as the latency for the user. Hence, caching at the edge exploits caching resources in order to save communication resources. In this thesis, we identify the fundamental problem of context-aware proactive caching for maximizing the number of cache hits under missing knowledge about content popularity. We introduce a new model for context-aware proactive caching that takes into account that different users may favor different content and that the users' preferences may depend on their contexts. Using a machine-learning-based approach based on contextual multi-armed bandits (contextual MAB), we propose a novel online learning algorithm for context-aware proactive caching. Based on its decentralized architecture of decision making, this algorithm enables the controller of a local cache to learn context-specific content popularity, which is typically not available a priori, online over time. The proposed algorithm takes the cache operator's objective into account by allowing for service differentiation. We analyze the computational complexity as well as the memory and communication requirements of the algorithm, and we show how the algorithm can be extended to practical requirements. Moreover, we derive a sublinear upper bound on the regret of the algorithm, which characterizes the learning speed and proves that the algorithm converges to the optimal cache content placement strategy. Simulations based on real data show that, depending on the cache size, the proposed algorithm achieves up to 27% more cache hits than the best algorithm taken from the literature. Mobile crowdsourcing (MCS) allows task owners to outsource tasks via a mobile crowdsourcing platform (MCSP) to a set of workers. Hence, MCS exploits user resources for task solving. In this thesis, we identify the fundamental problem of context-aware worker selection for maximizing the worker performance in MCS under missing knowledge about expected worker performance. We present a novel model for context-aware worker selection in MCS that can cope with different task types and that explicitly allows worker performance to be a non-linear function of both task and worker context. Using a machine-learning-based approach based on contextual MABs, we propose a new context-aware hierarchical online learning algorithm for worker selection in MCS. Based on the proposed hierarchical architecture of decision making, this algorithm splits information collection and decision making among several entities. Local controllers (LCs) in the workers' mobile devices learn the workers' context-specific performances online over time. The MCSP centrally assigns workers to tasks based on a regular information exchange with the LCs. This novel approach solves two critical aspects. First, personal worker context is kept locally in the LCs, which reduces communication overhead and preserves the privacy of the workers, who may not want to share personal context with the MCSP. Secondly, the MCSP is enabled to select the most capable workers for each task based on what the LCs learn about their workers' context-specific performances, which are typically unknown a priori. We analyze the computational complexity and derive upper bounds on the local memory requirements of the algorithm and on the number of times the quality of each worker must be assessed. Moreover, we show that the more access to worker context is granted to the LCs the lower are the communication requirements of the proposed algorithm compared to an equivalent centralized approach. In addition, we derive a sublinear upper regret bound, which characterizes the learning speed and proves that the algorithm converges to the optimal worker selection strategy. Finally, we show in simulations based on synthetic and real data that, depending on the availability of workers, the proposed algorithm achieves an up to 49% higher cumulative worker performance than the best algorithm from the literature

Clinically relevant preoperative anxiety as a predictor for the length of hospital stay of surgical patients

Abstrakt Einleitung: Die Assoziation von klinisch relevanter präoperativer Angst und der Krankenhausverweildauer (KVD) wurde bislang bei operativen Patienten nur wenig erforscht. Diese Studie untersucht den Zusammenhang der klinisch relevanten präoperativen Angst und der Krankenhausverweildauer von operativen Patienten aus verschiedenen chirurgischen Fachgebieten. Zusätzlich berücksichtigt wurden dabei Alter, Geschlecht, präoperativer Gesundheitszustand, somatische Komorbidität, operatives Fachgebiet und Schwere des operativen Eingriffs. Methodik: Die vorliegende prospektive Beobachtungsstudie ist eine Teilstudie des BRIA-Projekts (Brückenintervention in der Anästhesiologie) „Lebensstilbefragung von Patientinnen und Patienten in der Anästhesieambulanz“. Für die vorliegende Studie wurden die Daten von 2.612 Patienten ausgewertet. Die Daten wurden in den Anästhesieambulanzen der Klinik für Anästhesiologie mit Schwerpunkt operative Intensivmedizin der Charité - Universitätsmedizin Berlin im Zeitraum von Januar 2010 bis Juni 2010 am Campus Charité Mitte und am Campus Virchow-Klinikum mittels eines computerbasierten Fragebogens erhoben. Als Parameter der körperlichen Erholung wurde die Krankenhausverweildauer den elektronischen Datenverwaltungssystemen der Charité - Universitätsmedizin Berlin sechs Monate nach Datenerhebung entnommen. Ergebnisse: Ein multivariates logistisches Regressionsmodel mit der binären abhängigen Variablen „größer versus kleiner beziehungsweise gleich des Medians der Krankenhausverweildauer“ (Md=4 Tage) zeigte, dass ein statistisch signifikanter Zusammenhang zwischen klinisch relevanter präoperativer Angst und der Krankenhausverweildauer bestand (OR: 1.574; 95% CI 1.176-2.107; p=0.002), wenn gleichzeitig die Kovariaten Alter, Geschlecht, präoperativer Gesundheitszustand, somatische Komorbidität, operatives Fachgebiet und die Schwere des operativen Eingriffs in das Regressionsmodell einbezogen wurden. Im einfachen Gruppenvergleich unterschieden sich die Gruppe der Patienten mit klinisch relevanter Angst versus die Gruppe ohne klinisch relevante Angst nur gering, aber statistisch signifikant (p=0.02) hinsichtlich der Krankenhausverweildauer. Schlussfolgerung: Die Daten zeigen, dass ein Zusammenhang von klinisch relevanter Angst präoperativ gemessen und der Krankenhausverweildauer unabhängig von dem Einfluss von Alter, Geschlecht, präoperativem Gesundheitszustand, somatischen Komorbidität, operativem Fachgebiet, sowie Schwere des operativen Eingriffs besteht. Als nächsten Schritt könnte man eine randomisierte Interventionsstudie durchführen, in der die Krankenhausverweildauer von zwei Patientengruppen verglichen werden, die beide klinisch relevante Angst angeben. Eine Patientengruppe würde ausreichend frühzeitig vor der Operation (z.B. 4 bis 2 Wochen) psychologisch betreut werden und eine Kontrollgruppe dürfte keine psychologische Betreuung erfahren. So ließe sich beurteilen, ob präoperative psychologische Betreuung zu einer Veränderung der Krankenhausverweildauer führen kann.Abstract Objectives: The association of clinically relevant preoperative anxiety and length of hospital stay (LOS) has rarely been examined in surgical patients. This study investigates whether clinically relevant preoperative anxiety shows an independent association with LOS in patients from various surgical fields after adjusting for age, gender, health status, medical comorbidity, surgical field and severity of surgery. Methods: This prospective observational study is part of the investigation “Lifestyle survey of patients in the preoperative anaesthesiological assessment clinic“ of the BRIA project (Bridging Intervention in Anaesthesiology). For this study a total sample of 2,612 surgical patients was included. Data were assessed with a computer-based questionnaire between January 2010 and June 2010 at the preoperative anaesthesiological assessment clinics of the Department of Anesthesiology and Intensive Care Medicine, Campus Charité Mitte and Campus Virchow-Klinikum, Charité – Universitaetsmedizin Berlin. As outcome parameter of physical recovery, the length of hospital stay was obtained six months after data assessment from the electronic patient management system of Charité - Universitaetsmedizin Berlin. Results: A multivariate logistic regression analysis with the binary dependent variable “above versus below or equal to the median of LOS” (Md=4 days) showed a statistically significant association between clinically relevant preoperative anxiety and LOS (OR: 1.574; 95% CI 1.176-2.107; p=0.002) when adjusting the regression model for the covariates age, gender, preoperative health status, somatic comorbidity, surgical field and severity of the surgical procedure. Although it was statistically significant (p=0.02), the simple difference of LOS between the groups with and without anxiety was rather small. Conclusion: The data show that an association of clinically relevant preoperative anxiety and LOS exists, independent from the influence of age, gender, preoperative health status, somatic comorbidity surgical field and severity of the surgical procedure. The next step could be a randomized controlled trial comparing the length of hospital stay of two patient groups with preoperative anxiety with equally burdened patients. Participants of the intervention group would undergo preoperative psychological treatment, and the comparison group would receive no psychological intervention. This investigation would allow further evaluation of the effects of psychotherapeutic support on the surgical outcome in the future

Numerical Optimization of an Open-Ended Coaxial Slot Applicator for the Detection and Microwave Ablation of Tumors

A multiobjective optimization method for a dual-mode microwave applicator is proposed. Dual-modality means that microwaves are used apart from the treatment, and also for the monitoring of the microwave ablation intervention. (1) The use of computational models to develop and improve microwave ablation applicator geometries is essential for further advances in this field. (2) Numerical electromagnetic–thermal coupled simulation models are used to analyze the performance of the dual-mode applicator in liver tissue; the sensitivity evaluation of the dual-mode applicator’s sensing mode constrains the set of optimal solutions. (3) Three Pareto-optimal design parameter sets are derived that are optimal in terms of applicator efficiency as well as volume and sphericity of the ablation zone. The resulting designs of the dual-mode applicator provide a suitable sensitivity to distinguish between healthy and tumorous liver tissue. (4) The optimized designs are presented and numerically characterized. An improvement on the performance of previously proposed dual-mode applicator designs is achieved. The multiphysical simulation model of electromagnetic and thermal properties of the applicator is applicable for future comprehensive design procedures