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INDUX-R Consortium gathers for the 4th plenary meeting to advance human-centric XR innovation
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CoDy: Counterfactual explainers for dynamic graphs
Get PDFTemporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capabilities in these domains, their complex architectures pose significant challenges for explainability. Counterfactual explanation methods provide a promising solution by illustrating how modifications to input graphs can influence model predictions. To address this challenge, we present CoDy—Counterfactual Explainer for Dynamic Graphs—a model-agnostic, instance-level explanation approach that identifies counterfactual subgraphs to interpret TGNN predictions. CoDy employs a search algorithm that combines Monte Carlo Tree Search with heuristic selection policies, efficiently exploring a vast search space of potential explanatory subgraphs by leveraging spatial, temporal, and local event impact information. Extensive experiments against state-of-the-art factual and counterfactual baselines demonstrate CoDy's effectiveness, with improvements of 16% in AUFSC+ over the strongest baseline. Our code is available at: https://github.com/daniel-gomm/CoDy
Quantum Catalytic Space
Get PDFSpace complexity is a key field of study in theoretical computer science. In the quantum setting there are clear motivations to understand the power of space-restricted computation, as qubits are an especially precious and limited resource. Recently, a new branch of space-bounded complexity called catalytic computing has shown that reusing space is a very powerful computational resource, especially for subroutines that incur little to no space overhead. While quantum catalysis in an information theoretic context, and the power of “dirty” qubits for quantum computation, has been studied over the years, these models are generally not suitable for use in quantum space-bounded algorithms, as they either rely on specific catalytic states or destroy the memory being borrowed. We define the notion of catalytic computing in the quantum setting and show a number of initial results about the model. First, we show that quantum catalytic logspace can always be computed quantumly in polynomial time; the classical analogue of this is the largest open question in catalytic computing. This also allows quantum catalytic space to be defined in an equivalent way with respect to circuits instead of Turing machines. We also prove that quantum catalytic logspace can simulate log-depth threshold circuits, a class which is known to contain (and believed to strictly contain) quantum logspace, thus showcasing the power of quantum catalytic space. Finally we show that both unitary quantum catalytic logspace and classical catalytic logspace can be simulated in the one-clean qubit model
Physiological responses to affective virtual coach design in a VR fear of heights consultation
Get PDFVirtual coaches in virtual reality (VR) offer scalable mental health treatment without an on-site therapist, yet their impact on psychophysiological responses remains unclear. We examine how VR content and coach design influence physiological measures, such as heart rate (HR) and electrodermal activity (EDA), in a therapeutic setting. 120 participants with a fear of heights interacted with a virtual coach that varied in facial warmth (with/without) and affirmative nods (with/without) during a virtual consultation, followed by a virtual height exposure. Physiological responses were recorded. Virtual heights exposure elicited significantly higher HR (p < 0.001, r = 0.347) and EDA (p = 0.003, r = 0.292), but also increased heart rate variability (HRV, p = 0.005, r = 0.272) compared to the VR consultation. Warm facial expressions increased EDA peak amplitudes () during the consultation and raised HRV during height exposure (). This study highlights VR coach design’s impact on physiological responses, emphasising the need for thoughtful emotional design to enhance therapeutic outcomes in automated VR therapies
Phasebook: a survey of selected open problems in phase retrieval
Get PDFPhase retrieval is an inverse problem that, on one hand, is crucial in many applications across imaging and physics, and, on the other hand, leads to deep research questions in theoretical signal processing and applied harmonic analysis. This survey paper is an outcome of the recent workshop Phase Retrieval in Mathematics and Applications (PRiMA) (held on August 5–9 2024 at the Lorentz Center in Leiden, The Netherlands) that brought together experts working on theoretical and practical aspects of the phase retrieval problem with the purpose to formulate and explore essential open problems in the field
