Gains from the upgrade of the cold neutron triple-axis spectrometer FLEXX at the BER-II reactor

The upgrade of the cold neutron triple-axis spectrometer FLEXX is described. We discuss the characterisation of the gains from the new primary spectrometer, including a larger guide and double focussing monochromator, and present measurements of the energy and momentum resolution and of the neutron flux of the instrument. We found an order of magnitude gain in intensity (at the cost of coarser momentum resolution), and that the incoherent elastic energy widths are measurably narrower than before the upgrade. The much improved count rate should allow the use of smaller single crystals samples and thus enable the upgraded FLEXX spectrometer to continue making leading edge measurements.Comment: 8 pages, 7 figures, 5 table

Single-machine scheduling with stepwise tardiness costs and release times

We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems

Shock compression of feldspars

Hugoniot data for oligoclase and microcline to 670 and 580 kb and release adiabat data for oligoclase were obtained by means of the inclined mirror and immersed-foil-reflected-light techniques, respectively. Oligoclase and microcline have Hugoniot elastic limits in the range of 40–55 and 80–85 kb. These limits increase slightly with increasing driving shock pressure. Above the elastic limit, extending to ∼300 and ∼400 kb, transition regions of anomalously high compression are observed for microcline and oligoclase. These data and the data of McQueen, Marsh, and Fritz for albitite and anorthosite indicate that at successively higher shock pressures within this region, the feldspars gradually transform to a high-pressure, high-density polymorph. This polymorph probably corresponds to the rutile-like hollandite structure obtained in high-pressure quenching experiments by Kume, Matsumoto, and Koizumi (in KAlGe_3O_8) and by Ringwood, Reid, and Wadsley (in KAlSo_3O_8, microcline). In the hollandite structure germanium or silicon is in octahedral coordination with oxygen. The zero-pressure density and the Birch-Murnaghan equation of state parameters for the adiabat and isotherm are calculated for the high-pressure polymorphs of oligoclase, microcline, anorthosite, and albitite. The release adiabat centered at 180 kb indicates that at this shock pressure some (∼15%) of the hollandite phase forms but apparently reverts to a lower-density phase on pressure release. Release adiabat curves centered at 272 and 417 kb and calculated postshock temperatures indicate that material of feldspar composition recovered from meteorite and laboratory impacts is converted to the hollandite structure upon shock compression; upon pressure release this material probably reverts to the low-density maskelynite form

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

A delta Scuti distance to the Large Magellanic Cloud

We present results from a well studied delta Scuti star discovered in the LMC. The absolute magnitude of the variable was determined from the PL relation for Galactic delta Scuti stars and from the theoretical modeling of the observed B,V,I light curves. The two methods give distance moduli for the LMC of 18.46+-0.19 and 18.48+-0.15, respectively, for a consistent value of the stellar reddening of E(B-V)=0.08+-0.02. We have also analyzed 24 delta Scuti candidates discovered in the OGLE II survey of the LMC, and 7 variables identified in the open cluster LW 55 and in the galaxy disk by Kaluzny et al. (2003, 2006). We find that the LMC delta Scuti stars define a PL relation whose slope is very similar to that defined by the Galactic delta Scuti variables, and yield a distance modulus for the LMC of 18.50+-0.22 mag. We compare the results obtained from the delta Scuti variables with those derived from the LMC RR Lyrae stars and Cepheids. Within the observational uncertainties, the three groups of pulsating stars yield very similar distance moduli. These moduli are all consistent with the "long" astronomical distance scale for the Large Magellanic Cloud.Comment: Accepted for publication on A

Particlization in hybrid models

In hybrid models, which combine hydrodynamical and transport approaches to describe different stages of heavy-ion collisions, conversion of fluid to individual particles, particlization, is a non-trivial technical problem. We describe in detail how to find the particlization hypersurface in a 3+1 dimensional model, and how to sample the particle distributions evaluated using the Cooper-Frye procedure to create an ensemble of particles as an initial state for the transport stage. We also discuss the role and magnitude of the negative contributions in the Cooper-Frye procedure.Comment: 18 pages, 28 figures, EPJA: Topical issue on "Relativistic Hydro- and Thermodynamics"; version accepted for publication, typos and error in Eq.(1) corrected, the purpose of sampling and change from UrQMD to fluid clarified, added discussion why attempts to cancel negative contributions of Cooper-Frye are not applicable her

Sliding mode control of quantum systems

This paper proposes a new robust control method for quantum systems with uncertainties involving sliding mode control (SMC). Sliding mode control is a widely used approach in classical control theory and industrial applications. We show that SMC is also a useful method for robust control of quantum systems. In this paper, we define two specific classes of sliding modes (i.e., eigenstates and state subspaces) and propose two novel methods combining unitary control and periodic projective measurements for the design of quantum sliding mode control systems. Two examples including a two-level system and a three-level system are presented to demonstrate the proposed SMC method. One of main features of the proposed method is that the designed control laws can guarantee desired control performance in the presence of uncertainties in the system Hamiltonian. This sliding mode control approach provides a useful control theoretic tool for robust quantum information processing with uncertainties.Comment: 18 pages, 4 figure

Low cost high efficiency GaAs monolithic RF module for SARSAT distress beacons

Low cost high performance (5 Watts output) 406 MHz beacons are urgently needed to realize the maximum utilization of the Search and Rescue Satellite-Aided Tracking (SARSAT) system spearheaded in the U.S. by NASA. Although current technology can produce beacons meeting the output power requirement, power consumption is high due to the low efficiency of available transmitters. Field performance is currently unsatisfactory due to the lack of safe and reliable high density batteries capable of operation at -40 C. Low cost production is also a crucial but elusive requirement for the ultimate wide scale utilization of this system. Microwave Monolithics Incorporated (MMInc.) has proposed to make both the technical and cost goals for the SARSAT beacon attainable by developing a monolithic GaAs chip set for the RF module. This chip set consists of a high efficiency power amplifier and a bi-phase modulator. In addition to implementing the RF module in Monolithic Microwave Integrated Circuit (MMIC) form to minimize ultimate production costs, the power amplifier has a power-added efficiency nearly twice that attained with current commercial technology. A distress beacon built using this RF module chip set will be significantly smaller in size and lighter in weight due to a smaller battery requirement, since the 406 MHz signal source and the digital controller have far lower power consumption compared to the 5 watt power amplifier. All the program tasks have been successfully completed. The GaAs MMIC RF module chip set has been designed to be compatible with the present 406 MHz signal source and digital controller. A complete high performance low cost SARSAT beacon can be realized with only additional minor iteration and systems integration