The Effects of Product Ageing on Demand: The Case of Digital Cameras

The static differentiated product demand model when applied to products with rapid product turnover and declining prices, yields implausible results. One response is to explicitly model the inter-temporal choices of consumers but computational demands require restrictive assumptions on consumer heterogeneity and limits on the characteristics included in the model. We propose, instead, to supplement the static model with a control for the age that each product has been in the market. This approach is applied to the US digital camera market and we find we obtain more plausible estimates. Our results are consistent with inter-temporal price discrimination by firms. Furthermore, our results suggest that ignoring the effects of product ageing may result in substantially overestimated price elasticities and technological progress and underestimated price-cost markups.Discrete Choice; Demand Dynamics; Forward-Looking Behavior; Heterogeneous Preferences

On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy

Restart strategy helps the covariance matrix adaptation evolution strategy (CMA-ES) to increase the probability of finding the global optimum in optimization, while a single run CMA-ES is easy to be trapped in local optima. In this paper, the continuous non-revisiting genetic algorithm (cNrGA) is used to help CMA-ES to achieve multiple restarts from different sub-regions of the search space. The CMA-ES with on-line search history-assisted restart strategy (HR-CMA-ES) is proposed. The entire on-line search history of cNrGA is stored in a binary space partitioning (BSP) tree, which is effective for performing local search. The frequently sampled sub-region is reflected by a deep position in the BSP tree. When leaf nodes are located deeper than a threshold, the corresponding sub-region is considered a region of interest (ROI). In HR-CMA-ES, cNrGA is responsible for global exploration and suggesting ROI for CMA-ES to perform an exploitation within or around the ROI. CMA-ES restarts independently in each suggested ROI. The non-revisiting mechanism of cNrGA avoids to suggest the same ROI for a second time. Experimental results on the CEC 2013 and 2017 benchmark suites show that HR-CMA-ES performs better than both CMA-ES and cNrGA. A positive synergy is observed by the memetic cooperation of the two algorithms.Comment: 8 pages, 9 figure

Forget Demonstrations, Focus on Learning from Textual Instructions

This work studies a challenging yet more realistic setting for zero-shot cross-task generalization: demonstration-free learning from textual instructions, presuming the existence of a paragraph-style task definition while no demonstrations exist. To better learn the task supervision from the definition, we propose two strategies: first, to automatically find out the critical sentences in the definition; second, a ranking objective to force the model to generate the gold outputs with higher probabilities when those critical parts are highlighted in the definition. The joint efforts of the two strategies yield state-of-the-art performance on the challenging benchmark. Our code will be released in the final version of the paper.Comment: Preprin

Design and Simulation of a Novel Submerged Pressure Differential Wave Energy Converter for Optimized Energy Harvesting Efficiency and Performance

A novel submerged pressure differential wave energy converter (SPDWEC) has been designed and simulated for energy harvesting under both regular waves and irregular ocean waves. As the waves pass by, the oscillating water pressure on the flexible surface of the SPDWEC moves the pistons of the power take-off (PTO) system, in such a way the wave energy is converted into electricity. Hydrodynamic responses of the SPDWEC are simulated by a numerical model calculating both the linear wave forces and the nonlinear effect of wave height reduction caused by energy extraction. The results show that the SPDWEC can reach a high power capture ratio through system optimization of the stiffness and damping of the PTO system. This innovative SPDWEC exhibits improved lifetime and maintainability by enclosing the PTO inside the WaveHouse, where the overall air pressure keeps nearly constant. As shown in Figure 1, the optimal power capture ratio of the SPDWEC ranges from 0.21 to 0.32, which means the PTO system can extract 20-30% of the incident wave energy. The ideal power capture ratio, which does not consider the nonlinear effect caused by energy extraction, is much larger than the optimal power capture ratio and is larger than one for wave periods larger than 9 s. Please click Additional Files below to see the full abstract

Experimental study on nanoparticle deposition in straight pipe flow

Loss of the number of nanoparticles within pipe may lead to significant change of particle number distribution, total mass concentration and particles mean size. The experiments of multiple dispersion aerosol particles ranging from 5.6 nm to 560 nm in straight pipe are carried out using a fast mobility particle sizer. The particle size number distribution, total number concentrations, geometric mean size and volume are acquired under different pipe lengths and Reynolds numbers. The results show lengthening the pipe and strengthening the turbulence can promote the particle deposition process. The penetration efficiency of smaller particle is lower than the larger one, so the particle mean size increases in the process of deposition