In this paper, we survey the current state-ofart models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron (AP), Structured SVMs (SV M struct ), Max Margin Markov Networks (M 3 N), and an integration of search and learning algorithm (SEARN). With all due tuning efforts of various parameters of each model, on the data sets we have applied the models to, we found that SVM struct enjoys better performance compared with the others. In addition, we also propose a new method which we call the Structured Learning Ensemble (SLE) to combine these structured learning models. Empirical results show that our SLE algorithm provides more accurate solutions compared with the best results of the individual models.
Crossdocking studies have mostly been concerned with the physical layout of a crossdock or on a single crossdock. In this work, we study a network of crossdocks taking into consideration delivery and pickup time windows, warehouse capacities and inventory-handling costs. Because of the complexity of the problem, local search techniques are developed and used with simulated annealing and tabu search heuristics. Extensive experiments were conducted and results show that the heuristics outperform CPLEX, providing solutions in realistic timescales.
We report the first measurement of nighttime atmospheric temperature and horizontal wind profiles in the lower thermosphere up to 140 km with the Na lidar at Andes Lidar Observatory in Cerro Pachón, Chile (30.25°S, 70.74°W), when enhanced thermospheric Na was observed. Temperature and horizontal wind were derived up to 140 km using various resolutions, with the lowest resolution of about 2.7 hr and 15 km above 130 km. Thus, the measurements span 60 km in vertical, more than double the traditional 25 km. On the night of 17 April 2015, the horizontal wind magnitude in the thermosphere exceeds 150 ms−1, consistent with past rocket measurements. The meridional wind shows a clear transition from the diurnal‐tide‐dominant mesopause to the semidiurnal‐tide‐dominant lower thermosphere. A lidar with a 100 times the power aperture product will be able to measure wind and temperature above 160 km and cover longer time span, providing key measurements for the study of atmosphere‐space interactions in this region.
Dissipating waves contribute to vertical mixing of the atmosphere, alter molecular and eddy diffusion, and induce chemical transport of reactive species. These processes induce strong vertical transport of atmospheric constituents in regions where wave dissipation is significant. The effective wave diffusivity is proportional to the Stokes drift velocity imparted by the spectrum of vertically propagating waves, which is related to the vertical heat and wave energy fluxes. Because the heat flux cannot be derived from wave parameterization schemes employed in most atmospheric models, wave‐driven constituent transport has not been fully incorporated. However, we show in this paper that wave diffusivity can also be expressed in terms of the eddy diffusivity and variances of the temperature and lapse rate fluctuations, quantities that are readily derived from many wave parameterizations. The theory is in good agreement with lidar measurements of heat fluxes in the mesopause region. Total dynamical diffusivity associated with dissipating waves and turbulence can exceed 300 m2/s near the mesopause.
In this paper, we study a bidding problem which can be modeled as a set packing problem. A simulated annealing heuristic with three local moves, including an embedded branch-and-bound move, is developed for the problem. We compared the heuristic with the CPLEX 8.0 solver and the current best non-exact method, Casanova, using the standard CATS benchmark and other realistic test sets. Results show that the heuristic outperforms CPLEX and Casanova.
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