No Arabic abstract
The promise of any small improvement in the performance of light-harvesting devices, is sufficient to drive enormous experimental efforts. However these efforts are almost exclusively focused on enhancing the power conversion efficiency with specific material properties and harvesting layers thickness, without exploiting the correlations present in sunlight -- in part because such correlations are assumed to have negligible effect. Here we show, by contrast, that these spatio-temporal correlations are sufficiently relevant that the use of specific detector geometries would significantly improve the performance of harvesting devices. The resulting increase in the absorption efficiency, as the primary step of energy conversion, may also act as a potential driving mechanism for artificial photosynthetic systems. Our analysis presents design guidelines for optimal detector geometries with realistic incident intensities based on current technological capabilities
The space-time correlations of streams of photons can provide fundamentally new channels of information about the Universe. Todays astronomical observations essentially measure certain amplitude coherence functions produced by a source. The spatial correlations of wave fields has traditionally been exploited in Michelson-style amplitude interferometry. However the technology of the past was largely incapable of fine timing resolution and recording multiple beams. When time and space correlations are combined it is possible to achieve spectacular measurements that are impossible by any other means. Stellar intensity interferometry is ripe for development and is one of the few unexploited mechanisms to obtain potentially revolutionary new information in astronomy. As we discuss below, the modern use of stellar intensity interferometry can yield unprecedented measures of stellar diameters, binary stars, distance measures including Cepheids, rapidly rotating stars, pulsating stars, and short-time scale fluctuations that have never been measured before.
Long-range speckle correlations play an essential role in wave transport through disordered media, but have rarely been studied in other complex systems. Here we discover spatio-temporal intensity correlations for an optical pulse propagating through a multimode fiber with strong random mode coupling. Positive long-range correlations arise from multiple scattering in fiber mode space and depend on the statistical distribution of arrival times. By optimizing the incident wavefront of a pulse, we maximize the power transmitted at a selected time, and such control is significantly enhanced by the long-range spatio-temporal correlations. We provide an explicit relation between the correlations and the enhancements, which closely agrees with experimental data. Our work shows that multimode fibers provide a fertile ground for studying complex wave phenomena, and the strong spatio-temporal correlations can be employed for efficient power delivery at a well-defined time.
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
Photosynthetic systems utilize adaptability to respond efficiently to fluctuations in their light environment. As a result, large photosynthetic yields can be achieved in conditions of low light intensity, while photoprotection mechanisms are activated in conditions of elevated light intensity. In sharp contrast with these observations, current theoretical models predict bacterial cell death for physiologically high light intensities. To resolve this discrepancy, we consider a unified framework to describe three stages of photosynthesis in natural conditions, namely light absorption, exciton transfer and charge separation dynamics, to investigate the relationship between the statistical features of thermal light and the Quinol production in bacterial photosynthesis. This approach allows us to identify a mechanism of photoprotection that relies on charge recombination facilitated by the photon bunching statistics characteristic of thermal sunlight. Our results suggest that the flexible design underpinning natural photosynthesis may therefore rely on exploiting the temporal correlations of thermal light, manifested in photo-bunching patterns, which are preserved for excitations reaching the reaction center.
This paper investigates trajectory prediction for robotics, to improve the interaction of robots with moving targets, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be predicted with regression-based fitting procedures, therefore we apply state of the art machine learning, specifically based on Long-Short Term Memory (LSTM) architectures. In addition, fast moving targets are better sensed using event cameras, which produce an asynchronous output triggered by spatial change, rather than at fixed temporal intervals as with traditional cameras. We investigate how LSTM models can be adapted for event camera data, and in particular look at the benefit of using asynchronously sampled data.