Lay Rationalism and Inconsistency between Predicted Experience and Decision

Decision-makers are sometimes depicted as impulsive and overly influenced by ‘hot’, affective factors. The present research suggests that decision-makers may be too ‘cold’ and overly focus on rationalistic attributes, such as economic values, quantitative specifications, and functions. In support of this proposition, we find a systematic inconsistency between predicted experience and decision. That is, people are more likely to favor a rationalistically-superior option when they make a decision than when they predict experience. We discuss how this work contributes to research on predicted and decision utilities; we also discuss when decision-makers overweight hot factors and when they overweight cold factors. Copyright © 2003 John Wiley & Sons, Ltd

An evolutionary algorithm with double-level archives for multiobjective optimization

Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Neural Pairwise Ranking Factorization Machine for Item Recommendation

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Multi-view Self-supervised Disentanglement for General Image Denoising

With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen noise distributions, existing approaches often suffer from generalisation to unseen noise types or general and real noise. It is understandable as the model is designed to learn paired mapping (e.g. from a noisy image to its clean version). In this paper, we instead propose to learn to disentangle the noisy image, under the intuitive assumption that different corrupted versions of the same clean image share a common latent space. A self-supervised learning framework is proposed to achieve the goal, without looking at the latent clean image. By taking two different corrupted versions of the same image as input, the proposed Multi-view Self-supervised Disentanglement (MeD) approach learns to disentangle the latent clean features from the corruptions and recover the clean image consequently. Extensive experimental analysis on both synthetic and real noise shows the superiority of the proposed method over prior self-supervised approaches, especially on unseen novel noise types. On real noise, the proposed method even outperforms its supervised counterparts by over 3 dB

Multi-view Self-supervised Disentanglement for General Image Denoising

With its significant performance improvements, the deep learning paradigm has become a standard tool for modern image denoisers. While promising performance has been shown on seen noise distributions, existing approaches often suffer from generalisation to unseen noise types or general and real noise. It is understandable as the model is designed to learn paired mapping (e.g. from a noisy image to its clean version). In this paper, we instead propose to learn to disentangle the noisy image, under the intuitive assumption that different corrupted versions of the same clean image share a common latent space. A self-supervised learning framework is proposed to achieve the goal, without looking at the latent clean image. By taking two different corrupted versions of the same image as input, the proposed Multi-view Self-supervised Disentanglement (MeD) approach learns to disentangle the latent clean features from the corruptions and recover the clean image consequently. Extensive experimental analysis on both synthetic and real noise shows the superiority of the proposed method over prior self-supervised approaches, especially on unseen novel noise types. On real noise, the proposed method even outperforms its supervised counterparts by over 3 dB.Comment: International Conference on Computer Vision 2023 (ICCV 2023

Comparative study on the thermal performance and economic efficiency of vertical and horizontal ground heat exchangers

The ground-coupled heat pump is a shallow geothermal exploitation method taking soil as the thermal energy source. The ground heat exchanger is an important component of this system, which includes vertical or horizontal configurations. However, to the best of our knowledge, few studies exist involving the comparison of thermal performances and installation costs of two heat exchanger types considering the influence of ground climate, which makes the selection of heat exchanger configuration challenging for a specific field application. Hence, a 3-dimensional numerical model considering the variations of atmospheric conditions and soil water content is constructed in this paper. Based on this model, the thermal performances and economical efficiencies of vertical and horizontal ground heat exchangers are compared. The results indicate that the thermal performance difference between the two heat exchangers is greater in winter than in summer. The thermal performance is hardly influenced by the injection mass flow rate, while it is considerably affected by the length of heat exchanger. The thermal power rises linearly with the increase in heat exchanger length, and the increment of the vertical ground heat exchanger is higher. In addition, when the heat exchanger length is shorter than 40 m, the installation cost and thereby the total cost of the horizontal ground heat exchanger is considerably higher. With regard to both the thermal performance and economic efficiency, a vertical ground heat exchanger is only recommended when installing a single shallow ground heat exchanger.Cited as: Cui, Q., Shi, Y., Zhang, Y., Wu, R., Jiao, Y. Comparative study on the thermal performance and economic efficiency of vertical and horizontal ground heat exchangers. Advances in Geo-Energy Research, 2023, 7(1): 7-19. https://doi.org/10.46690/ager.2023.01.0