Functional renormalization group study of an eight-band model for the iron arsenides

We investigate the superconducting pairing instabilities of eight-band models for the iron arsenides. Using a functional renormalization group treatment, we determine how the critical energy scale for superconductivity depends on the electronic band structure. Most importantly, if we vary the parameters from values corresponding to LaFeAsO to SmFeAsO, the pairing scale is strongly enhanced, in accordance with the experimental observation. We analyze the reasons for this trend and compare the results of the eight-band approach to those found using five-band models.Comment: 11 pages, 10 figure

Simple overdense rf plasma source

A simple, gas‐fed, radio‐frequency‐driven plasma source is described. By use of lower hybrid waves, noble gas plasmas were produced with electron densities up to 10^12 cm -3 over a range of magnetic fields from 400 G to 1.5 kG and rf frequencies from 2–220 MHz

The production of highly unidirectional lower-hybrid waves

The development of a highly unidirectional lower-hybrid wave source would improve the electron current drive efficiency in tokamaks. Lower-hybrid waves launched from a phased wave array are shown to be reflected from a grid placed in a cold, low-density plasma. The antenna-grid combination results in highly unidirectional lower-hybrid waves

Time Scale for Rapid Draining of a Surficial Lake Into the Greenland Ice Sheet

A 2008 report by Das et al. documented the rapid drainage during summer 2006 of a supraglacial lake, of approximately 44×10^6 m^3, into the Greenland ice sheet over a time scale moderately longer than 1 hr. The lake had been instrumented to record the time-dependent fall of water level and the uplift of the ice nearby. Liquid water, denser than ice, was presumed to have descended through the sheet along a crevasse system and spread along the bed as a hydraulic facture. The event led two of the present authors to initiate modeling studies on such natural hydraulic fractures. Building on results of those studies, we attempt to better explain the time evolution of such a drainage event. We find that the estimated time has a strong dependence on how much a pre-existing crack/crevasse system, acting as a feeder channel to the bed, has opened by slow creep prior to the time at which a basal hydraulic fracture nucleates. We quantify the process and identify appropriate parameter ranges, particularly of the average temperature of the ice beneath the lake (important for the slow creep opening of the crevasse). We show that average ice temperatures 5–7  °C below melting allow such rapid drainage on a time scale which agrees well with the 2006 observations

Optical properties of Southern Hemisphere aerosols: Report of the joint CSIRO/NASA study

This study was made in support of the LAWS and GLOBE programs, which aim to design a suitable Doppler lidar system for measuring global winds from a satellite. Observations were taken from 5 deg S to 45 deg S along and off the E and SE Australian coast, thus obtaining representative samples over a large latitude range. Observations were made between 0 and 6 km altitude of aerosol physical and chemical properties in situ from the CSIRO F-27 aircraft; of lidar backscatter coefficients at 10.6 micron wavelength from the F-27 aircraft; of lidar backscatter profiles at 0.694 microns at Sale, SE Australia; and of lidar backscatter profiles at 0.532 microns at Cowley Beach, NE Australia. Both calculations and observations in the free troposphere gave a backscatter coefficient of 1-2 x 10 to the -11/m/sr at 10.6 microns, although the accuracies of the instruments were marginal at this level. Equivalent figures were 2-8 x 10 to the -9/m/sr (aerosol) and 9 x 10 to the -9 to 2 x 10 to the -8/m/sr (lidar) at 0.694 microns wavelength at Sale; and 3.7 x 10 to the -9/m/sr (aerosol) and 10 to the -8 to 10 to the -7/m/sr (lidar) at 0.532 microns wavelength at Cowley Beach. The measured backscatter coefficients at 0.694 and 0.532 microns were consistently higher than the values calculated from aerosol size distributions by factors of typically 2 to 10

Profiles and trajectories of mental health service utilisation during early intervention in psychosis

Background: Early intervention in psychosis services (EIS) support individuals experiencing a first episode of psychosis. Support required will vary in response to the remittance and reoccurrence of symptoms, including relapses. Characterising individuals who will need more intensive support can inform care planning. This study explores service utilisation profiles and their trajectories of service use in a sample of individuals referred to EIS. Method: We analysed service utilisation during the 3 years following referral to EIS (n = 2363) in West London between 2011 and 2020. Mental health service utilisation data were submitted to model-based clustering. Latent growth models were then estimated for identified profiles. Profiles were compared regarding clinical and demographic characteristics and onward pathways of care. Results: Analyses revealed 5 profiles of individuals attending EIS based on their service utilisation over 3 years. 55.5% of the sample were members of a low utilisation and less clinically severe profile. The distinct service use patterns of these profiles were associated with Health of the Nations Outcome Scale scores at treatment initiation (at total, subscale, and individual item level), along with age and gender. These patterns of use were also associated with onward care and ethnicity. Conclusions: Profiles and trajectories of service utilisation call for development of integrated care pathways and use of more personalised interventions. Services should consider patient symptoms and characteristics when making clinical decisions informing the provision of care. The profiles represent typical patterns of service use, and identifying factors associated with these subgroups might help optimise EIS support

Alarm-Based Prescriptive Process Monitoring

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

Validity of numerical trajectories in the synchronization transition of complex systems

We investigate the relationship between the loss of synchronization and the onset of shadowing breakdown {\it via} unstable dimension variability in complex systems. In the neighborhood of the critical transition to strongly non-hyperbolic behavior, the system undergoes on-off intermittency with respect to the synchronization state. There are potentially severe consequences of these facts on the validity of the computer-generated trajectories obtained from dynamical systems whose synchronization manifolds share the same non-hyperbolic properties.Comment: 4 pages, 4 figure