IRCAM/Radio-France Cassettes

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Causal structure in the presence of sectorial constraints, with application to the quantum switch

Existing work on quantum causal structure assumes that one can perform arbitrary operations on the systems of interest. But this condition is often not met. Here, we extend the framework for quantum causal modelling to cases where a system can suffer \textit{sectorial contraints}, that is, restrictions on the orthogonal subspaces of its Hilbert space that may be mapped to one another. Our framework (a) proves that a number of different intuitions about causal relations turn out to be equivalent; (b) shows that quantum causal structures in the presence of sectorial constraints can be represented with a directed graph; and (c) defines a fine-graining of the causal structure in which the individual sectors of a system bear causal relations, which provides a more detailed analysis than its coarse-grained counterpart. As an example, we apply our framework to purported photonic implementations of the quantum switch to show that while their coarse-grained causal structure is cyclic, their fine-grained causal structure is acyclic. We therefore conclude that these experiments realize indefinite causal order only in a weak sense. Notably, this is the first argument to this effect that is not rooted in the assumption that the causal relata must be localized in spacetime

Application of Distributed Wireless Chloride Sensors to Environmental Monitoring: Initial Results

Over the next 30 years, it is anticipated that the world will need to source 70% more food to provide for the growing population, and it is likely that a significant amount of this will have to come from irrigated land. However, the quality of irrigation water is also important, and measuring the quality of this water will allow management decisions to be made. Soil salinity is an important parameter in crop yield, and in this paper, we describe a chloride sensor system based on a low-cost robust screen-printed chloride ion sensor, suitable for use in distributed sensor networks. Previously, this sensor has been used in controlled laboratory-based experiments, but here we provide evidence that the sensor will find application outside of the laboratory in field deployments. We report on three experiments using this sensor; one with a soil column, one using a fluvarium, and finally on an experiment in a greenhouse. All these give an insight into the movement of chloride over small distances with high temporal resolution. These initial experiments illustrate that the new sensors are viable and usable with relatively simple electronics, and although subject to ongoing development, they are currently capable of providing new scientific data at high spatial and temporal resolutions. Therefore, we conclude that such chloride sensors, coupled with a distributed wireless network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water, with significant cost and resource saving

Improving Lake Mixing Process Simulations in the Community Land Model by Using K Profile Parameterization

We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification

Revision of Erpetosuchus (Archosauria: Pseudosuchia) and new erpetosuchid material from the Late Triassic ‘Elgin Reptile’ fauna based on µCT scanning techniques

The Late Triassic fauna of the Lossiemouth Sandstone Formation (LSF) from the Elgin area, Scotland, has been pivotal in expanding our understanding of Triassic terrestrial tetrapods. Frustratingly, due to their odd preservation, interpretations of the Elgin Triassic specimens have relied on destructive moulding techniques, which only provide incomplete, and potentially distorted, information. Here, we show that micro-computed tomography (μCT) could revitalise the study of this important assemblage. We describe a long-neglected specimen that was originally identified as a pseudosuchian archosaur, Ornithosuchus woodwardi. μCT scans revealed dozens of bones belonging to at least two taxa: a small-bodied pseudosuchian and a specimen of the procolophonid Leptopleuron lacertinum. The pseudosuchian skeleton possesses a combination of characters that are unique to the clade Erpetosuchidae. As a basis for investigating the phylogenetic relationships of this new specimen, we reviewed the anatomy, taxonomy and systematics of other erpetosuchid specimens from the LSF (all previously referred to Erpetosuchus). Unfortunately, due to the differing representation of the skeleton in the available Erpetosuchus specimens, we cannot determine whether the erpetosuchid specimen we describe here belongs to Erpetosuchus granti (to which we show it is closely related) or if it represents a distinct new taxon. Nevertheless, our results shed light on rarely preserved details of erpetosuchid anatomy. Finally, the unanticipated new information extracted from both previously studied and neglected specimens suggests that fossil remains may be much more widely distributed in the Elgin quarries than previously recognised, and that the richness of the LSF might have been underestimated

Profitable and Sustainable Grazing Systems for Livestock Producers with Saline Land in Southern Australia

Dryland salinity affects over 2.5 M ha in Australia, mostly in southern states and is expanding at 3-5% per year (NLWRA, 2001). The prognosis is for considerable expansion of the area affected by salinity and waterlogging (1217 M ha at equilibrium), because groundwater levels continue to rise and only small-scale land management programmes have been implemented. In addition, many waterways are increasingly saline, especially in the Murray Darling Basin and in Western Australia (WA). Sustainable Grazing on Saline Land (SGSL) addresses the need to make productive use of saline land and water resources. Its research component operates at 12 sites across WA, South Australia (SA), Victoria and New South Wales (NSW) and consists of coordinated activities that have regional relevance and contribute nationally. The programme seeks to develop and demonstrate profitable and sustainable grazing systems on saline land that have positive environmental and social impacts. Whilst there are different priority research issues at each site, data collection is governed by common measurement protocols for salt and water movement, biodiversity, and pasture and animal performance in order to make comparisons and data sharing across sites practical