Igor Letina, Shuo Liu, Nick Netzer, Optimal contest design: tuning the heat, Journal of Economic Theory, Vol. 213, 2023. (Journal Article)
We consider the design of contests when the principal can choose both the prize profile and how the prizes are allocated as a function of a possibly noisy signal about the agents' efforts. We provide sufficient conditions that guarantee optimality of a contest. Optimal contests have a minimally competitive prize profile and an intermediate degree of competitiveness in the contest success function. Whenever observation is not too noisy, the optimum can be achieved by an all-pay contest with a cap. When observation is perfect, the optimum can also be achieved by a nested Tullock contest. We relate our results to a recent literature which has asked similar questions but has typically focused on the design of either the prize profile or the contest success function. |
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Mateusz Dolata, Gerhard Schwabe, Moving beyond privacy and airspace safety: Guidelines for just drones in policing, Government Information Quarterly, Vol. 40 (4), 2023. (Journal Article)
The use of drones offers police forces potential gains in efficiency and safety. However, their use may also harm public perception of the police if drones are refused. Therefore, police forces should consider the perception of bystanders and broader society to maximize drones' potential. This article examines the concerns expressed by members of the public during a field trial involving 52 test participants. Analysis of the group interviews suggests that their worries go beyond airspace safety and privacy, broadly dis-cussed in existing literature and regulations. The interpretation of the results indicates that the perceived justice of drone use is a significant factor in acceptance. Leveraging the concept of organizational justice and data collected, we propose a catalogue of guidelines for just operation of drones to supplement the existing policy. We present the organizational justice perspective as a framework to integrate the concerns of the public and bystanders into legal work. Finally, we discuss the relevance of justice for the legitimacy of the police's actions and provide implications for research and practice. |
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Christoph Basten, Mike Mariathasan, Interest rate pass-through and bank risk-taking under negative-rate policies with tiered remuneration of central bank reserves, Journal of Financial Stability, Vol. 68, 2023. (Journal Article)
We identify the effects of negative rates on bank behavior using difference-in-differences identification. First, we find that going negative can interrupt not only the pass-through from policy to deposit but also to mortgage rates. To preserve their deposit franchise, banks finance negative deposit with increased mortgage spreads, the more the bigger their market power. Second, negative rates on reserves induce banks to cut some reserves without replacement and replace others with riskier assets. Together with increased mortgage spreads, balance sheet restructuring preserves profits but risk-taking increases. Third, pass-through interruption and risk-taking can be reduced through tiered remuneration. |
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Reto Eberle, Swiss Audit Monitor 2023: Analyse des Revisionsmarkts der kotierten Unternehmen in der Schweiz, Expert Focus, Vol. 2023 (Oktober), 2023. (Journal Article)
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Madhav Sachdeva, Jan Burmeister, Jörn Kohlhammer, Jürgen Bernard, LFPeers: Temporal similarity search and result exploration, Computers & Graphics, Vol. 115, 2023. (Journal Article)
In this paper, we introduce a general concept for the analysis of temporal and multivariate data and the system LFPeers that applies this concept to temporal similarity search and results exploration. The conceptual workflow divides the analysis in two phases: a search phase to find the most similar objects to a query object before a time point in the temporal data, and an exploration phase to analyze and contextualize this subset of objects after . LFPeers enables users to search for peers through interactive similarity search and filtering, explore interesting behavior of this peer group, and learn from peers through the assessment of diverging behaviors. We present the conceptual workflow to learn from peers and the LFPeers system with novel interfaces for search and exploration in temporal and multivariate data. An earlier workshop publication for LFPeers included a usage scenario targeting epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. In this extended paper, we now show how our concept is generalized and applied by domain experts in two case studies, including a novel case on stocks data. Finally, we reflect on the new state of development and on the insights gained by the experts in the case studies on the search and exploration of temporal data to learn from peers. |
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Alessandro Ferrari, Matteo Fiorini, Joseph Francois, Bernard Hoekman, Lisa Lechner, Miriam Manchin, Filippo Santi, EU trade agreements and non-trade policy objectives, In: Coherence of the European Union trade policy with its non-trade objectives: World Trade Forum, Cambridge University Press, Cambridge, p. 180 - 207, 2023-10. (Book Chapter)
The EU’s common commercial policy is used as an instrument to realize its values in EU trading partners, reflected in the inclusion of sustainable trade and development chapters in EU preferential trade agreements (PTAs). This chapter asks if including non-trade provisions (NTPs) in EU PTAs has a systematic positive effect on non-trade outcomes in partner countries. It analyzes the relationship between bilateral trade flows, the coverage of NTPs in EU PTAs and the performance of EU partner countries on several non-trade outcome variables using synthetic control methods. It finds no robust evidence of a causal effect of including NTPs in EU PTAs on indicators of non-trade outcomes. |
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Ling Gao, Hang Sun, Daniel Gehrig, Marco Cannici, Davide Scaramuzza, Laurent Kneip, A 5-Point Minimal Solver for Event Camera Relative Motion Estimation, In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023-10-01. (Conference or Workshop Paper published in Proceedings)
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feature extraction and dynamics estimation are tightly coupled when using event cameras, and no precise model is currently available for describing the complex structures generated by lines in the space-time volume of events. We solve this problem by deriving the correct non-linear parametrization of such manifolds, which we term eventails, and demonstrate its application to eventbased linear motion estimation, with known rotation from an Inertial Measurement Unit. Using this parametrization, we introduce a novel minimal 5-point solver that jointly estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines. We demonstrate on both synthetic and real data that our solver generates more stable relative motion estimates than other methods while capturing more inliers than clustering based on spatiotemporal planes. In particular, our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%. The proposed eventails contribute to a better understanding of spatio-temporal event-generated geometries and we thus believe it will become a core building block of future event-based motion estimation algorithms. |
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Nikola Zubić, Daniel Gehrig, Mathias Gehrig, Davide Scaramuzza, From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection, In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Computer Vision Foundation, 2023-10-01. (Conference or Workshop Paper published in Proceedings)
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming. This work eliminates this bottleneck by selecting representations based on the Gromov-Wasserstein Discrepancy (GWD) between raw events and their representation. It is about 200 times faster to compute than training a neural network and preserves the task performance ranking of event representations across multiple representations, network backbones, datasets, and tasks. Thus finding representations with high task scores is equivalent to finding representations with a low GWD. We use this insight to, for the first time, perform a hyperparameter search on a large family of event representations, revealing new and powerful representations that exceed the state-of-the-art. Our optimized representations outperform existing representations by 1.7 mAP on the 1 Mpx dataset and 0.3 mAP on the Gen1 dataset, two established object detection benchmarks, and reach a 3.8% higher classification score on the mini N-ImageNet benchmark. Moreover, we outperform state-of-the-art by 2.1 mAP on Gen1 and state-of-the-art feed-forward methods by 6.0 mAP on the 1 Mpx datasets. This work opens a new unexplored field of explicit representation optimization for event-based learning. |
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Jiawei Fu, Yunlong Song, Yan Wu, Fisher Yu, Davide Scaramuzza, Learning Deep Sensorimotor Policies for Vision-Based Autonomous Drone Racing, In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Institute of Electrical and Electronics Engineers, 2023. (Conference or Workshop Paper published in Proceedings)
The development of effective vision-based algorithms has been a significant challenge in achieving autonomous drones, which promise to offer immense potential for many real-world applications. This paper investigates learning deep sensorimotor policies for vision-based drone racing, which is a particularly demanding setting for testing the limits of an algorithm. Our method combines feature representation learning to extract task-relevant feature representations from high-dimensional image inputs with a learning-by-cheating framework to train a deep sensorimotor policy for vision-based drone racing. This approach eliminates the need for globally-consistent state estimation, trajectory planning, and handcrafted control design, allowing the policy to directly infer control commands from raw images, similar to human pilots. We conduct experiments using a realistic simulator and show that our vision-based policy can achieve state-of-the-art racing performance while being robust against unseen visual disturbances. Our study suggests that consistent feature embeddings are essential for achieving robust control performance in the presence of visual disturbances. The key to acquiring consistent feature embeddings is utilizing contrastive learning along with data augmentation. |
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Jiaxu Xing, Giovanni Cioffi, Javier Hidalgo-Carrio, Davide Scaramuzza, Autonomous Power Line Inspection with Drones via Perception-Aware MPC, In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, Institute of Electrical and Electronics Engineers, 2023. (Conference or Workshop Paper published in Proceedings)
Drones have the potential to revolutionize power line inspection by increasing productivity, reducing inspection time, improving data quality, and eliminating the risks for human operators. Current state-of-the-art systems for power line inspection have two shortcomings: (i) control is decoupled from perception and needs accurate information about the location of the power lines and masts; (ii) obstacle avoidance is decoupled from the power line tracking, which results in poor tracking in the vicinity of the power masts, and, consequently, in decreased data quality for visual inspection. In this work, we propose a model predictive controller (MPC) that overcomes these limitations by tightly coupling perception and action. Our controller generates commands that maximize the visibility of the power lines while, at the same time, safely avoiding the power masts. For power line detection, we propose a lightweight learning-based detector that is trained only on synthetic data and is able to transfer zero-shot to real-world power line images. We validate our system in simulation and real-world experiments on a mock-up power line infrastructure. We release our code and datasets to the public. |
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Oscar Arce, Miguel García-Posada, Sergio Mayordomo, Steven Ongena, Bank lending policies and monetary policy: some lessons from the negative interest era, Economic Policy, Vol. 38 (116), 2023. (Journal Article)
What is the long-term impact of negative interest rates on bank lending? To answer this question, we construct a unique summary measure of negative rate exposure by individual banks based on exclusive survey data and banks’ balance sheets and couple it with the credit register of Spain and firms’ balance sheets to identify this impact on the supply of credit to firms. We find that only when deposit rates reached the zero lower bound did affected banks (relative to non-affected banks) decrease their supply, especially when undercapitalized and lending to risky firms. The adverse effects of the negative rates on banks’ intermediation capacity only took place after a protracted period of time. |
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Reto Eberle, Kurt Speck, «Am Beginn der Lernkurve»: Der Professor an der Universität Zürich und Partner bei KPMG zu den Herausforderungen der Branche, In: Handelszeitung, p. 51, 28 September 2023. (Newspaper Article)
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Haiyan Yang, Renato Pajarola, Visual-assisted Outlier Preservation for Scatterplot Sampling, In: VMV: Vision, Modeling, and Visualization, The Eurographics Association, 2023. (Conference or Workshop Paper published in Proceedings)
Scatterplot sampling has long been an efficient and effective way to resolve the overplotting issues commonly occurring in large-scale scatterplot visualization applications. However, it is challenging to preserve the existence of low-density points or outliers after sampling for a sub-sampling algorithm if, at the same time, faithfully representing the relative data densities is of importance. In this work, we propose to address this issue in a visual-assisted manner. While the whole dataset is sub-sampled, the density of the outliers is modeled and visually integrated into the final scatterplot together with the sub-sampled point data.
We showcase the effectiveness of our proposed method in various cases and user studies. |
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Loris Sauter, Heiko Schuldt, Raphael Waltenspül, Luca Rossetto, Novice-Friendly Text-based Video Search with vitrivr, In: CBMI 2023: 20th International Conference on Content-based Multimedia Indexing, ACM Digital library, 2023-09-20. (Conference or Workshop Paper published in Proceedings)
Video retrieval still offers many challenges which can so far only be effectively mediated through interactive, human-in-the-loop retrieval approaches. The vitrivr multimedia retrieval stack offers a broad range of query mechanisms to enable users to perform such interactive retrieval. While these multiple mechanisms offer various options to experienced users, they can be difficult to use for novices. In this paper, we present a minimal user interface geared towards novice users that only exposes a subset of vitrivr’s functionality but simplifies user interaction. |
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Oana Inel and
Nicolas Mattis and
Milda Norkute and
Alessandro Piscopo and
Timoth\'ee Schmude and
Sanne Vrijenhoek and
Krisztian Balog, QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender Systems, In: Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, ACM, 2023. (Conference or Workshop Paper)
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Peter Kuhn, Liudmila Zavolokina, Dian Balta, Florian Matthes, Toward Government as a Platform: An Analysis Method for Public Sector Infrastructure, In: 18th International Conference on Wirtschaftsinformatik, AIS Electronic Library (AISeL), Paderborn, Germany, 2023-09-18. (Conference or Workshop Paper published in Proceedings)
Government as a Platform (GaaP) is a promising approach to the digital transformation of the public sector. In practice, GaaP is realized by platform-oriented infrastructures. However, despite successful examples, the transformation toward platform-oriented infrastructures remains challenging. A potential remedy is the analysis of existing public infrastructure regarding its platform orientation. Such an analysis can identify the gaps to an ideal platform-oriented infrastructure and, thus, support the transformation toward it. We follow the design science research methodology to develop a four-dimensional analysis method. We do so in three iterations, and, after each iteration, evaluate the method by its application to infrastructures in practice. With regard to theory, our results suggest extending GaaP conceptualizations with a specific emphasis on platform principles. With regard to practice, we contribute an analysis method that creates proposals for the improvement of infrastructures and, thus, supports the transformation toward GaaP. |
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Stefania Gavrila-Ionescu, Aniko Hannak, Nicolo Pagan, Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations, In: RecSys '23: 17th ACM Conference on Recommender Systems, ACM Digital library, 2023-09-18. (Conference or Workshop Paper published in Proceedings)
The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems. |
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Xingzhen Zhu, Markus Lang, Helmut Max Dietl, Content Quality Assurance on Media Platforms with User-Generated Content, Journal of Theoretical and Applied Electronic Commerce Research, Vol. 18 (3), 2023. (Journal Article)
This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms’ optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model’s results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy. |
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Lucien Heitz, Juliane A Lischka, Rana Abdullah, Laura Laugwitz, Hendrik Meyer, Abraham Bernstein, Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study, In: RecSys '23: Seventeenth ACM Conference on Recommender Systems, ACM Digital library, 2023-09-18. (Conference or Workshop Paper published in Proceedings)
News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under scrutiny. In the area of personalized news, many platforms make design choices driven by economic incentives. In contrast to such systems that optimize for financial gain, there can be norm-driven diversity systems that prioritize normative and democratic goals. However, their impact on users in terms of inducing behavioral change or influencing knowledge is still understudied. In this paper, we contribute to the field of news recommender system design by conducting a user study that examines the impact of these normative approaches. We a.) operationalize the notion of a deliberative public sphere for news recommendations, show b.) the impact on news usage, and c.) the influence on political knowledge, attitudes and voting behavior. We find that exposure to small parties is associated with an increase in knowledge about their candidates and that intensive news consumption about a party can change the direction of attitudes of readers towards the issues of the party. |
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Dietmar Harhoff, Patrick Lehnert, Curdin Pfister, Uschi Backes-Gellner, Innovation effects and knowledge complementarities in a diverse research landscape, In: International Conference on Technical and Vocational Education and Training. 2023. (Conference Presentation)
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