Outputs (218)

Toward a framework for Responsible AI in storytelling for nonprofit fundraising (2024)
Book Chapter
Herrero, M., & Concannon, S. (2024). Toward a framework for Responsible AI in storytelling for nonprofit fundraising. In G. Ugazio, & M. Maricic (Eds.), . Routledge. https://doi.org/10.4324/9781003468615-11

AI techniques offer novel and compelling possibilities for nonprofit fundraising (e.g., data science applications can provide a deeper understanding of audiences and donors, and generative methods can create more personalized and persuasive communica... Read More about Toward a framework for Responsible AI in storytelling for nonprofit fundraising.

Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images (2024)
Presentation / Conference Contribution
Toso, M., Fiorini, S., James, S., & Del Bue, A. (2025, March). Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images. Presented at International Conference on 3D Vision (3DV), Singapore

World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billion entries, including distinctive landmarks and common urban objects. A... Read More about Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images.

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation (2024)
Journal Article
Zhu, C., Zhang, L., Luo, W., Jiang, G., & Wang, Q. (2025). Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Networks, 181, Article 106859. https://doi.org/10.1016/j.neunet.2024.106859

Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o... Read More about Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers (2024)
Journal Article
Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430

Objectives: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model... Read More about From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment (2024)
Presentation / Conference Contribution
Zhou, K., Wang, L., Zhang, X., Shum, H. P., Li, F. W. B., Li, J., & Liang, X. (2024, September). MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment. Presented at Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Milan, Italy

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the mi... Read More about MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment.

Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis (2024)
Presentation / Conference Contribution
Isaac-Medina, B., Gaus, Y., Bhowmik, N., & Breckon, T. (2024, September). Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis. Presented at ECCV 2024: European Conference on Computer Vision, Milan, Italy

Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approach... Read More about Towards Open-World Object-Based Anomaly Detection viaSelf-Supervised Outlier Synthesis.

ARCTIC: Approximate Real-Time Computing in a Cache-Conscious Multicore Environment (2024)
Journal Article
Saha, S., Chakraborty, S., Agarwal, S., Själander, M., & McDonald-Maier, K. D. (2024). ARCTIC: Approximate Real-Time Computing in a Cache-Conscious Multicore Environment. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 43(10), 2944-2957. https://doi.org/10.1109/tcad.2024.3384442

Improving result-accuracy in approximate computing (AC)-based time-critical systems, without violating power constraints of the underlying circuitry, is gradually becoming challenging with the rapid progress in technology scaling. The execution span... Read More about ARCTIC: Approximate Real-Time Computing in a Cache-Conscious Multicore Environment.

Declarative Lifecycle Management in Digital Twins (2024)
Presentation / Conference Contribution
Bencomo, N., Kamburjan, E., Tapia Tarifa, S. L., & Broch-Johnsen, E. (2024, September). Declarative Lifecycle Management in Digital Twins. Presented at 1st International Conference on Engineering Digital Twins (EDTconf 2024), Linz, Austria

Together, a digital twin and its physical counterpart can be seen as a self-adaptive system: the digital twin monitors the physical system, updates its own internal model of the physical system, and adjusts the physical system by means of controllers... Read More about Declarative Lifecycle Management in Digital Twins.

Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination (2024)
Journal Article
Jiang, C., Wang, S., Long, Y., Li, Z., Zhang, H., & Shao, L. (2025). Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(3), 1395-1413. https://doi.org/10.1109/tpami.2024.3487631

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of seen primitives. Prior studies have attempted to either learn primitives individually (non-connected) or establish dependencies among them in the composition (fully-conne... Read More about Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination.

The Riis Complexity Gap for QBF Resolution (2024)
Journal Article
Beyersdorff, O., Clymo, J., Dantchev, S., & Martin, B. (2024). The Riis Complexity Gap for QBF Resolution. Journal on Satisfiability, Boolean Modeling and Computation, 15(1), 9-25. https://doi.org/10.3233/sat-231505

We give an analogue of the Riis Complexity Gap Theorem in Resolution for Quantified Boolean Formulas (QBFs). Every first-order sentence ϕ without finite models gives rise to a sequence of QBFs whose minimal refutations in tree-like QBF Resolution sys... Read More about The Riis Complexity Gap for QBF Resolution.