Optical diode based on the chirality of guided photons

Photons are nonchiral particles: their handedness can be both left and right. However, when light is transversely confined, it can locally exhibit a transverse spin whose orientation is fixed by the propagation direction of the photons. Confined photons thus have chiral character. Here, we employ this to demonstrate nonreciprocal transmission of light at the single-photon level through a silica nanofibre in two experimental schemes. We either use an ensemble of spin-polarised atoms that is weakly coupled to the nanofibre-guided mode or a single spin-polarised atom strongly coupled to the nanofibre via a whispering-gallery-mode resonator. We simultaneously achieve high optical isolation and high forward transmission. Both are controlled by the internal atomic state. The resulting optical diode is the first example of a new class of nonreciprocal nanophotonic devices which exploit the chirality of confined photons and which are, in principle, suitable for quantum information processing and future quantum optical networks

Dispersive Optical Interface Based on Nanofiber-Trapped Atoms

We dispersively interface an ensemble of one thousand atoms trapped in the evanescent field surrounding a tapered optical nanofiber. This method relies on the azimuthally-asymmetric coupling of the ensemble with the evanescent field of an off-resonant probe beam, transmitted through the nanofiber. The resulting birefringence and dispersion are significant; we observe a phase shift per atom of $\sim$\,1\,mrad at a detuning of six times the natural linewidth, corresponding to an effective resonant optical density per atom of 0.027. Moreover, we utilize this strong dispersion to non-destructively determine the number of atoms.Comment: 4 pages, 4 figure

Wetlands and Coal Surface Mining: A Management Handbook

As the third phase of a three-year project, this report outlines management options for protecting wetlands during the surface mining of coal, particularly for the portion of the Eastern Interior Coal Region that is found in Kentucky, Indiana, and Illinois. It is presented in manual form for use by coal mine operators, regulatory agencies and research institutions. The previous phases of the project produced an atlas of the most heavily-mined areas of the western Kentucky coal field, which classified and identified wetlands in these areas, and discussed some specific impacts of mining on these wetlands. The need to present information that will lead to action by coal operations and regulatory agencies to protect wetland areas, is the incentive for this report. The main issues addressed in this the manual include: basic information for identifying wetlands; wetland values, and methods used for values assessment; how coal surface mining can affect wetlands; a method for addressing wetland protection needs and some prevention and mitigation actions; reclamation alternatives, including wetland restoration and the creation of wetlands as alternative ecosystems on mined areas; and general legal and regulatory information concerning wetland protection and surface mining of coal. Information was gathered through a search of current literature and by contact with state and federal agencies, some coal mining operations, and other concerned organizations. A detailed listing of places to go for more information is included as an appendix

Estimation and comparison of gross primary productivity patterns in created riparian wetlands

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A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics

We consider the problem of extracting safe environments and controllers for reach-avoid objectives for systems with known state and control spaces, but unknown dynamics. In a given environment, a common approach is to synthesize a controller from an abstraction or a model of the system (potentially learned from data). However, in many situations, the relationship between the dynamics of the model and the \textit{actual system} is not known; and hence it is difficult to provide safety guarantees for the system. In such cases, the Standard Simulation Metric (SSM), defined as the worst-case norm distance between the model and the system output trajectories, can be used to modify a reach-avoid specification for the system into a more stringent specification for the abstraction. Nevertheless, the obtained distance, and hence the modified specification, can be quite conservative. This limits the set of environments for which a safe controller can be obtained. We propose SPEC, a specification-centric simulation metric, which overcomes these limitations by computing the distance using only the trajectories that violate the specification for the system. We show that modifying a reach-avoid specification with SPEC allows us to synthesize a safe controller for a larger set of environments compared to SSM. We also propose a probabilistic method to compute SPEC for a general class of systems. Case studies using simulators for quadrotors and autonomous cars illustrate the advantages of the proposed metric for determining safe environment sets and controllers.Comment: 22nd ACM International Conference on Hybrid Systems: Computation and Control (2019

Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep

Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Use of accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection

Willow and cottonwood development adjacent to planted and unplanted freshwater marshes

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