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Bright triplet and bright charge-separated singlet excitons in organic diradicals enable optical read-out and writing of spin states
No full textOptical control of electron spin states is important for quantum sensing and computing applications, as developed with the diamond nitrogen vacancy centre. This requires electronic excitations, excitons, with net spin. Here we report a molecular diradical where two trityl radical groups are coupled via a meta-linked fluorene bridge. The singlet exciton is at lower energy than the triplet because electron transfer from one of the radical non-bonding orbitals to the other is spin allowed, set by the charging energy for the double occupancy of the non-bonding level, the Hubbard U. Both excitons give efficient photoluminescence at 640 and 700 nm with near unity efficiency. The ground state exchange energy is low, 60 μeV, allowing control of ground state spin populations. We demonstrate spin-selective intersystem crossing and show coherent microwave control. We report up to 8% photoluminescence contrast at microwave resonance. This tuning of the singlet Mott–Hubbard exciton against the ‘bandgap’ exciton provides a new design platform for spin–optical materials
A Comparative Analysis of Cardiac Sympathetic and Parasympathetic Activity in Sleep Using Linear and Nonlinear Indices
No full textSleep is characterized by notable decreases in physiological functions such as heart rate and blood pressure, which are also mediated by adaptations in cardiac sympathetic (SA) and parasympathetic activity (PA). This study aimed to investigate the differences in such SA and PA between wakefulness and sleep using both linear and nonlinear approaches. Employing a Laguerre expansion of the Wiener-Volterra autoregressive kernels of heart rate variability (HRV) series, we derived the sympathetic activity index (SAI) and parasympathetic activity index (PAI), along with their ratio. In the frequency domain, we derived the power in the low-frequency (LF) and high-frequency (HF) bands, and also calculated their ratio. On their time-resolved estimation, we also analyzed nonlinear features, including short-scaling ex-ponent α1 through detrended fluctuation analysis. Data were gathered in 18 healthy subjects undergoing a full-night sleep recording. Results show significant differences in SAI and PAI during wakefulness and sleep states, with higher SAI variability observed during wakefulness, and increased PAI during sleep. Notably, nonlinear analysis revealed a higher sympathetic α2 during sleep, suggesting increased complexity in sympathetic activity. These findings underscore the importance of parasympathetic activity during a sleep state, also associated with greater short-scale sympathetic complexity. Further research is needed to explore sleep stage-specific changes and pathological observations.Clinical relevance: Characterizing sleep-specific sympathetic and parasympathetic dynamics is crucial in clinical investigations. The application of nonlinear indices may open new perspectives for understanding autonomic regulation during sleep
Early Diagnosis of Non‐Cystic Fibrosis Bronchiectasis in Childhood: Shedding Light on Barriers and Opportunities
No full textBackground: "Bronchiectasis" is a clinical syndrome characterized by chronic wet cough associated with chronic mucus production and abnormal bronchial dilatation. This condition is currently diagnosed through high resolution chest tomography in both adults and children. Bronchiectasis continues to be underdiagnosed -especially in childhood- but this age group should be considered of particular importance since the progression from chronic bronchitis to bronchiectasis may take many years. Methods: In this paper we analyze the barriers to a timely diagnosis of non-CF bronchiectasis in childhood and discuss new options to improve the diagnostic algorithms as well as new opportunities for patients. Conclusions: Bronchiectasis diagnosis should be done as early as possible in children, since interrupting the infection/inflammation cycle is critical to reverse and/or stop the disease progression and structural lung injury. Further studies are required to establish transition programs from pediatric to adult bronchiectasis care
Collaborative Neural Painting
No full textThe process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the painting process. We introduce a novel task, Collaborative Neural Painting (CNP), to facilitate collaborative art painting generation between users and agents. Given any number of user-input brushstrokes as the context or just the desired object class, CNP should produce a sequence of strokes supporting the completion of a coherent painting. Importantly, the process can be gradual and iterative, so allowing users’ modifications at any phase until the completion. Moreover, we propose to solve this task using a painting representation based on a sequence of parametrized strokes, which makes it easy both editing and composition operations. These parametrized strokes are processed by a Transformer-based architecture with a novel attention mechanism to model the relationship between the input strokes and the strokes to complete. We also propose a new masking scheme to reflect the interactive nature of CNP and adopt diffusion models as the basic learning process for its effectiveness and diversity in the generative field. Finally, to develop and validate methods on the novel task, we introduce a new dataset of painted objects and an evaluation protocol to benchmark CNP both quantitatively and qualitatively. We demonstrate the effectiveness of our approach and the potential of the CNP task as a promising avenue for future research. Project page and code: this https URL
International experiences of systems approaches: re-thinking policies and governance to transform agrifood systems
No full textA profound transformation of purpose means that agrifood systems are expected to attain multiple sustainability outcomes, beyond producing enough food, towards achieving human and planetary health for current and future generations. Yet, despite the existing range of policies, innovations and interventions, agrifood systems transformation is hindered by short-term thinking, siloed approaches, power imbalances and linear mindsets. Persistent challenges demand a transformation of how action is taken. In response, people and institutions across the world are beginning to adopt different ways of working. Drawing inspiration from various countries, this article illustrates the promise and practice of delivering agrifood systems transformation through a systems approach. Key insights confirm that first, systems transformation requires long-term programmatic and investment cycles that leverage the interconnectedness of the agrifood system. Second, effective transformation needs to value the role of systems leaders to catalyse the change process, while also enabling inclusive governance processes that empower a diversity of voices to participate in decision-making. Finally, tangible outputs (e.g. change in policy or governance body or investment) and intangible outputs (e.g. change in thinking, relationships, connections and agency) of a systems approach are observed. Future agrifood systems interventions should promote both types of outputs, as essential components of transformation. This article is part of the theme issue 'Transforming terrestrial food systems for human and planetary health'
La disinformazione, i contenuti illegali e i limiti alla libertà di espressione online: un’inevitabile evaporazione delle garanzie costituzionali?
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Weak Formulation for Physics-Informed Neural Networks in the Resolution of Analysis Problems in Electromagnetics
No full textA new class of neural networks has been recently introduced to solve problems based on some physical model. Such networks are called “Physical-Informed Neural Networks” (PINN). PINN are trained not based on input-output data, but rather by explicitly enforcing the model physical laws. More in detail, they are trained to minimize the “equations residual” in the physical model in each point of the domain rather than the discrepancy with known data. In this contribution, we propose to formulate the equations of ElectroMagnetism (EM) embedded in a PINN by using a weak formulation approach. This helps convergence of the training process, thanks to the lower derivation order required in the error computation. In addition, the presence of different materials in the domain is easily treated. In this digest, the approach is described in its fundamentals, and a simple example is presented