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Recent Submissions 

  1. Deformation Capacity and Ductility of Shear Connectors 

    Baertschi, Roland; Garcia, Samuel; Kroyer, Robert; et al. (2023)
    ce/papers
    Deformation capacity and slip range (eventually called “ductility”) of shear connectors are key parameters for the design of shear connectors in composite beams, especially in partial shear connection. Existing design rules require a deformation capacity of at least 6 mm for the use of shear connectors with plastic design methods. This rule is combined with the assumption of rigid-ideal plastic behaviour of the shear connectors. Still, ...
    Conference Paper
  2. Provably Powerful Graph Neural Networks for Directed Multigraphs 

    Egressy, Béni; von Niederhäusern, Luc; Blanusa, Jovan; et al. (2024)
    This paper analyses a set of simple adaptations that transform standard message-passing Graph Neural Networks (GNN) into provably powerful directed multigraph neural networks. The adaptations include multigraph port numbering, ego IDs, and reverse message passing. We prove that the combination of these theoretically enables the detection of any directed subgraph pattern. To validate the effectiveness of our proposed adaptations in practice, ...
    Conference Paper
  3. CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs 

    Grötschla, Florian; Mathys, Joël; Veres, Robert; et al. (2024)
    The Twelfth International Conference on Learning Representations (ICLR 2024)
    Graph Visualization, also known as Graph Drawing, aims to find geometric embeddings of graphs that optimize certain criteria. Stress is a widely used metric; stress is minimized when every pair of nodes is positioned at their shortest path distance. However, stress optimization presents computational challenges due to its inherent complexity and is usually solved using heuristics in practice. We introduce a scalable Graph Neural Network ...
    Conference Paper
  4. Unravelling Expressive Delegations: Complexity and Normative Analysis 

    Tyrovolas, Giannis; Constantinescu, Andrei; Elkind, Edith (2024)
    We consider binary group decision-making under a rich model of liquid democracy recently proposed by Colley, Grandi, and Novaro (2022): agents submit ranked delegation options, where each option may be a function of multiple agents' votes; e.g., "I vote yes if a majority of my friends vote yes." Such ballots are unravelled into a profile of direct votes by selecting one entry from each ballot so as not to introduce cyclic dependencies. ...
    Conference Paper
  5. Dissecting the EIP-2930 Optional Access Lists 

    Heimbach, Lioba; Kniep, Quentin; Vonlanthen, Yann; et al. (2024)
    Ethereum introduced Transaction Access Lists (TALs) in 2020 to optimize gas costs during transaction execution. In this work, we present a comprehensive analysis of TALs in Ethereum, focusing on adoption, quality, and gas savings. Analyzing a full month of mainnet data with 31,954,474 transactions, we found that only 1.46% of transactions included a TAL, even though 42.6% of transactions would have benefited from it. On average, access ...
    Conference Paper
  6. The PoW Landscape in the Aftermath of The Merge 

    Kiffer, Lucianna; Skorik, Sophia; Vonlanthen, Yann; et al. (2024)
    On 15th September 2022, The Merge marked the Ethereum network's transition from computation-hardness-based consensus (proof-of-work) to a committee-based consensus mechanism (proof-of-stake). As a result, all the specialized hardware and GPUs that were being used by miners ceased to be profitable in the main Ethereum network. Miners were then left with the decision of how to re-purpose their hardware. One such choice was to try and make ...
    Other Conference Item
  7. The Role of Facial and Speech Features in Emotion Classification 

    Houmard, Loic; Kastrati, Ard; Vasilevski, Dushan; et al. (2024)
    Conference Paper
  8. High-Bandwidth Fourier-Transform Spectrometer Monolithically Integrated on Thin Film Lithium Niobate 

    Finco, Giovanni; Li, Gaoyuan; Maeder, Andreas; et al. (2023)
    Other Conference Item
  9. Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training 

    Doucet, Paul; Estermann, Benjamin; Aczel, Till; et al. (2024)
    This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained models. We introduce a straightforward heuristic called TCM that mitigates the cold start problem while maintaining strong performance across various data levels. By initially applying TypiClust for diversity sampling and subsequently transitioning to uncertainty ...
    Other Conference Item
  10. Efficient and Scalable Graph Generation through Iterative Local Expansion 

    Bergmeister, Andreas; Martinkus, Karolis; Perraudin, Nathanaël; et al. (2024)
    In the realm of generative models for graphs, extensive research has been conducted. However, most existing methods struggle with large graphs due to the complexity of representing the entire joint distribution across all node pairs and capturing both global and local graph structures simultaneously. To overcome these issues, we introduce a method that generates a graph by progressively expanding a single node to a target graph. In each ...
    Conference Paper
  11. Ethereum Proof-of-Stake Consensus Layer: Participation and Decentralization 

    Grandjean, Dominic; Heimbach, Lioba; Wattenhofer, Roger (2023)
    In September 2022, Ethereum transitioned from Proof-of-Work (PoW) to Proof-of-Stake (PoS) during "the merge" - making it the largest PoS cryptocurrency in terms of market capitalization. With this work, we present a comprehensive measurement study of the current state of the Ethereum PoS consensus layer on the beacon chain. We perform a longitudinal study of the history of the beacon chain. Our work finds that all dips in network participation ...
    Conference Paper
  12. Optimus: Warming Serverless ML Inference via Inter-Function Model Transformation 

    Hong, Zicong; Lin, Jian; Guo, Song; et al. (2024)
    Conference Paper
  13. SoK: Attacks on DAOs 

    Feichtinger, Rainer; Fritsch, Robin; Heimbach, Lioba; et al. (2024)
    Other Conference Item
  14. GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks 

    Müller, Peter; Faber, Lukas; Martinkus, Karolis; et al. (2024)
    Conference Paper
  15. SUPClust: Active Learning at the Boundaries 

    Ono, Yuta; Aczel, Till; Estermann, Benjamin; et al. (2024)
    Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel active learning method called SUPClust that seeks to identify points at the decision boundary between classes. By targeting these points, SUPClust aims to gather information that is most informative for refining the model's prediction of complex decision regions. ...
    Other Conference Item
  16. A Framework for the Design and Evaluation of Architectural Tilesets 

    Zhao, Hanbing; Savov, Anton; Zhang, Hang; et al. (2023)
    eCAADe ~ Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2
    Generative design, increasingly prevalent in architecture, enables design exploration and enhanced productivity compared to traditional methods. Researchers have investigated combinatorial design using tilesets, which encode architectural meaning and promote user-friendly interactions. However, most research focuses on discovering designs rather than fine-tuning tilesets. We propose a tile-based method that introduces metrics for evaluating ...
    Conference Paper
  17. Non-Atomic Arbitrage in Decentralized Finance 

    Heimbach, Lioba; Pahari, Vabuk; Schertenleib, Eric (2024)
    The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage ...
    Conference Paper
  18. Identifying the Design Feature That Causes Project Delay in DfMA: A Dominant Element Analysis Method for Project Scheduling 

    Cao, Jianpeng; Zhang, Hang; Pan, Bo; et al. (2024)
    Computing in Civil Engineering 2023: Visualization, Information Modeling, and Simulation
    Design for manufacturing and assembly (DfMA) is an engineering methodology which aims to increase ease of manufacture and efficiency of assembly by considering manufacturing and assembly constraints in the design process. However, current DfMA approaches in the construction sector are not automated enough to identify the design features that may cause project delay in real time. This leads to longer design cycle. Also, current scheduling ...
    Conference Paper
  19. Resilient Urban Design Prototypes with Guidelines of the Coastal City Under Extreme Climate Change: The Case Study of Singapore 

    Zhang, Yiwei; Stouffs, Rudi (2023)
    Design for Climate Adaptation
    Conference Paper
  20. FPGA Resource-aware Structured Pruning for Real-Time Neural Networks 

    Ramhorst, Benjamin; Loncar, Vladimir; Constantinides, George A. (2023)
    2023 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE TECHNOLOGY, ICFPT
    Neural networks achieve state-of-the-art performance in image classification, speech recognition, scientific analysis and many more application areas. Due to the high computational complexity and memory footprint of neural networks, various compression techniques, such as pruning and quantization, have been proposed in literature. Pruning sparsifies a neural network, reducing the number of multiplications and memory. However, pruning often ...
    Conference Paper

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