Patient centricity and participation

"It is a truth universally acknowledged by policy makers, researchers and research funding bodies that patients and the public should be 'involved' in research, though there are different perspectives on what such involvement should look like and why it should happen"[Greenhalgh et al., 2019]. 1 The survey of Greenhalgh et al. [2019] lists three main arguments: (1) "patients have a right to have an input to research on their condition and that reducing the known power imbalances between researchers and patients is a moral duty of researchers"; (2) "patient and public involvement, by bringing a real-world and lived-experience perspective, improves the efficiency and value of research via a number of mechanisms: increasing its relevance to patients; improving recruitment and retention rates of research participants; extending the range of people represented in research studies; and improving dissemination of findings beyond academic audiences"; (3) "forming alliances with patients and the public is a defining feature of contemporary Mode 2 science in which knowledge is co-constructed by scientists and citizens, often beyond the walls of the university", in daily practice also known as co-design [Loiselle, 2023] (also see Gibbons et al. [2010] for an introduction to Mode 2 science).</p

The structure of Precambrian crust in sub-Saharan Africa:An AfricaArray synthesis and review

We combine new estimates of crustal thickness and shear wave velocities from 48 broadband seismic stations in Mozambique, Namibia, South Africa and Uganda with previously published results to review and examine Precambrian crustal structure in sub-Saharan Africa for secular trends. The ensemble of crustal structure estimates used relies heavily on data obtained through the AfricaArray initiative, which is briefly reviewed. Whether or not Precambrian crustal structure exhibits notable changes from the Mesoarchean through the Neoproterozoic places a key constraint on continental crustal genesis and evolution. Our 48 new estimates of Moho depth and crustal shear wave velocity profiles, combined with results from similar previous studies, yield an average crustal thickness for all Precambrian terranes of 39 ± 4 km. We find that average crustal thicknesses are essentially identical for Mesoarchean (38 ± 3 km), Neoarchean (39 ± 4 km), Paleoproterozoic (40 ± 4 km), Mesoproterozoic (40 ± 4 km) and Neoproterozoic (39 ± 4 km) terranes. The average thickness of the mafic lower crust, identified by high velocity layering (Vs &gt; 4.0 km/s), is also almost identical in Archean and Proterozoic terranes (7 ± 4 km and 6 ± 4 km, respectively). Finally, the average crustal shear wave velocities for all terranes fall within 1 standard deviation of a mean velocity of 3.7 km/s. These results are consistent with findings from other studies highlighting a lack of evidence for secular variation in crustal structure or composition within Precambrian terranes in sub-Saharan Africa, suggesting that secular trends, if they existed at the time of crust formation, have been obscured by crustal reworking during later orogenic and/or magmatic events.</p

Functional regression for space‐time prediction of precipitation‐induced shallow landslides in South Tyrol, Italy

Landslides are geomorphic hazards in mountainous terrains across the globe, driven by a complex interplay of static and dynamic controls. Data-driven approaches have been employed to assess landslide occurrence at regional scales by analyzing the spatial aspects and time-varying conditions separately. However, the joint assessment of landslides in space and time remains challenging. This study aims to predict the occurrence of precipitation-induced shallow landslides in space and time within the Italian province of South Tyrol (7,400 km2). We introduce a functional predictor framework where precipitation is represented as a continuous time series, in contrast to conventional approaches that treat precipitation as a scalar predictor. Using hourly precipitation data and past landslide occurrences from 2012 to 2021, we implemented a functional generalized additive model to derive statistical relationships between landslide occurrence, various static scalar factors, and the preceding hourly precipitation as a functional predictor. We evaluated the resulting predictions through several cross-validation routines, yielding performance scores frequently exceeding 0.90. To demonstrate the model predictive capabilities, we performed a hindcast for a storm event in the Passeier Valley on 4–5 August 2016, capturing the observed landslide locations and illustrating the hourly evolution of the predicted probabilities. Compared to standard early warning approaches, this framework eliminates the need to predefine fixed time windows for precipitation aggregation while inherently accounting for lagged effects. By integrating static and dynamic controls, this research advances the prediction of landslides in space and time for large areas, addressing seasonal effects and underlying data limitations

Milliwatt-level UV generation using sidewall poled lithium niobate

Integrated coherent sources of ultra-violet (UV) light are essential for a wide range of applications, from ion-based quantum computing and optical clocks to gas sensing and microscopy. Conventional approaches that rely on UV gain materials face limitations in terms of wavelength versatility; in response frequency upconversion approaches that leverage various optical nonlinearities have received considerable attention. Among these, the integrated thin-film lithium niobate (TFLN) photonic platform shows particular promise owing to lithium niobate's transparency into the UV range, its strong second order nonlinearity, and high optical confinement. However, to date, the high propagation losses and lack of reliable techniques for consistent poling of cm-long waveguides with small poling periods have severely limited the utility of this platform. Here we present a sidewall poled lithium niobate (SPLN) waveguide approach that overcomes these obstacles and results in a more than two orders of magnitude increase in generated UV power compared to the state-of-the-art. Our UV SPLN waveguides feature record-low propagation losses of 2.3 dB/cm, complete domain inversion of the waveguide cross-section, and an optimum 50% duty cycle, resulting in a record-high normalized conversion efficiency of 5050 %W$^{-1}$cm$^{-2}$, and 4.2 mW of generated on-chip power at 390 nm wavelength. This advancement makes the TFLN photonic platform a viable option for high-quality on-chip UV generation, benefiting emerging applications

Optimizing excited states in quantum Monte Carlo:A reassessment of double excitations

Quantum Monte Carlo (QMC) methods have proven to be highly accurate for computing excited states, but the choice of optimization strategies for multiple states remains an active topic of investigation. In this work, we revisit the calculation of double excitation energies in nitroxyl, glyoxal, tetrazine, and cyclopentadienone, exploring different objective functionals and their impact on the accuracy and robustness of QMC. A previous study for these systems employed a penalty functional to enforce orthogonality among the states, but the chosen prefactors did not strictly ensure convergence to the target states. Here, we confirm the reliability of previous results by comparing excitation energies obtained with different functionals and analyzing their consistency. In addition, we investigate the performance of different functionals when starting from a pre-collapsed excited state, providing insight into their ability to recover the target wave functions.</p

Adaptive XAI:Advancing Intelligent Interfaces for Tailored AI Explanations (2nd Edition)

As artificial intelligence becomes increasingly embedded in daily decision-making processes, the need for effective communication between humans and AI systems grows more crucial. The Adaptive XAI (AXAI) workshop, now in its second edition, focuses on developing intelligent interfaces that can adaptively explain AI’s decision-making processes. Building on the success of our inaugural event at IUI 2024, this workshop continues to explore the intersection of Explainable AI and adaptive user interfaces, emphasizing the development of interfaces that dynamically adapt to create explanations that resonate with diverse users. In line with the human-centric principles of the Future Artificial Intelligence Research (FAIR) project, we examine how emerging technologies such as conversational agents and Large Language Models can enhance AI explainability while ensuring explanations remain malleable and responsive to users’ evolving cognitive states and contextual needs.</p

Do ImageNet-trained models learn shortcuts?:The impact of frequency shortcuts on generalization

Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image datasets often exploit such shortcuts, potentially impairing their generalization performance. However, existing methods for identifying frequency shortcuts require expensive computations and become impractical for analyzing models trained on large datasets. In this work, we propose the first approach to more efficiently analyze frequency shortcuts at a large scale. We show that both CNN and transformer models learn frequency shortcuts on ImageNet. We also expose that frequency shortcut solutions can yield good performance on out-of-distribution (OOD) test sets which largely retain texture information. However, these shortcuts, mostly aligned with texture patterns, hinder model generalization on rendition-based OOD test sets. These observations suggest that current OOD evaluations often overlook the impact of frequency shortcuts on model generalization. Future benchmarks could thus benefit from explicitly assessing and accounting for these shortcuts to build models that generalize across a broader range of OOD scenarios

Engineering Morphologies of Metal-Based Colloidal Assemblies via Colloid Jamming at Liquid-Liquid Interfaces

Self-assemblies, structured via nanoparticles, show promise as materials for advanced applications, like photonic devices, electrochemical energy storage units and catalysis support. Despite observing diverse morphologies, a comprehensive understanding of the formation mechanism remains elusive. In this work, we show that the coordination interaction between metal-based sulfide nanoparticles (MS NPs) and the fluorosurfactants at the droplet interface influences the morphology during the evaporation-induced self-assembly facilitated by droplet microfluidics. Further investigation into fluorosurfactants with various chemical groups and MS NPs reveals that the strength of coordination interactions significantly influences assembly morphology. The interfacial interactions can be eliminated through coating a SiO2 layer on the metal-based colloid (M@SiO2 NPs). In addition, we demonstrate that the morphologies of the self-assemblies can be engineered via the coordination interactions between the MS NPs and fluorosurfactants, and by varying the concentrations of MS NPs. Utilizing these interfacial interactions, assemblies with core-shell and homogeneous distribution of binary nanoparticles were constructed. Our findings offer novel insights into the interfacial jamming of nanoparticles at the droplet interface through evaporation-induced self-assembly, and into the design of metal-based colloidal assemblies with diverse morphologies, crucial for developing novel functional assemblies for catalysis, plasmonic, and porous materials in a controlled manner

Hybrid Schrödinger-Liouville and projective dynamics

Quantum dynamics provides the arguably most fundamental example of hybrid dynamics: As long as no measurement takes place, the system state is governed by the Schr\"odinger-Liouville differential equation, which is however interrupted and replaced by projective dynamics at times when measurements take place. We show how this alternatingly continuous and projective evolution can be cast in form of one single differential equation for a refined state space manifold and thus be made amenable to standard port-theoretic analysis and control techniques