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Morris, P.

Publications and source records attributed to Morris, P..

The Accretion Disk of the Lithium-Depleted Young Binary St 34

We presented the infrared spectrum of the young binary system St 34 obtained with the Infrared Spectrograph (IRS) on the Spitzer Space Telescope. The IRS spectrum clearly shows excess dust emission, consistent with the suggestion of White & Hillenbrand that St 34 is accreting from a circumbinary disk. The disk emission of St 34 is low in comparison with the levels observed in typical T Tauri stars; silicate features at 10 and 20 microns are much weaker than typically seen in T Tauri stars; and excess emission is nearly absent at the shortest wavelengths observed. These features of the infrared spectrum suggest substantial grain growth (to eliminate silicate features) and possible settling of dust to the disk midplane (to reduce the continuum excess emission levels), along with a relatively evacuated inner disk, as expected due to gravitational perturbations by the binary system. Although the position of St 34 in the H-R diagram suggests an age of 8f Myr, assuming that it lies at the distance of the Taurus-Auriga molecular clouds, White & Hillenbrand could not detect any Li I absorption, which would indicate a Li depletion age of roughly 25 Myr or more. We suggest that St 34 is closer than the Taurus clouds by about 30-40 pc and has an age roughly consistent with Li depletion models. Such an advanced age would make St 34 the oldest known low-mass pre-main-sequence object with a dusty accretion disk. The persistence of optically thick dust emission well outside the binary orbit may indicate a failure to make giant planets that could effectively remove dust particles.

Hartmann, Lee↗

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Rossi, F.↗

Two Solvers for Tractable Temporal Constraints with Preferences

A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time on the basis of preferences. Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. We describe two solvers based on two different approaches for solving the same tractable subclass. For each solver we present the theoretical results it stands on, a description of the algorithm and some experimental results. The random generator used to build the problems on which tests are performed is also described. Finally, we compare the two solvers highlighting the tradeoff between performance and representational power.

Rossi, F.↗

Solving and Learning Soft Temporal Constraints: Experimental Scenario and Examples

Soft temporal constraint problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preference use machine learning techniques which learn the local preferences from the global ones. In this paper we describe the existing framework for both solving and learning preferences in temporal constraint problems, the implemented modules, the experimental scenario, and preliminary results on some examples.

Rossi, F.↗

Measurements of stratospheric NO, NO2, and N2O5 by ISAMS: Preliminary observations and data validation

The Improved Stratospheric and Mesospheric Sounder (ISAMS) is a multichannel radiometer and forms part of the science payload of the Upper Atmosphere Research Satellite (UARS). ISAMS measures infrared emissions from the Earth's atmosphere in several wavelength bands. Three such bands include emission from nitric oxide, nitrogen dioxide, and dinitrogen pentoxide. In this paper, we briefly discuss how the ISAMS instrument measures NO, NO2, and N2O5. We also present preliminary data from these channels and describe preliminary validation work.

Kerridge, Brian J.↗

Measurements of stratospheric constituents by ISAMS

ISAMS is a limb sounding radiometer flying on the UARS, and designed to measure temperature, pressure, O3, CO, NO, NO2, N2O5, HNO3, CH4, H2O, N2O, and aerosol. Its capabilities are described, together with the present status of validation of its data products, and plans for future improvement.

Rodgers, Clive D.↗

D region predictions

Present knowledge of D region phenomena is briefly reviewed and the status of current methods of predicting their effects on radio propagation considered. The ELF, VLF and LF navigational and timing systems depend on the stability of the lower part of the D layer where these waves are reflected, whereas MF and HF waves are absorbed as they penetrate the region, in most cases mainly in the upper part of the layer. Possible methods of improving predictions, warnings, and real time operations are considered with particular stress on those which can be implemented in the near future.

Thrane, E. V.↗