Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

How Flexible is Your Computing System?

In literature, computer architectures are frequently claimed to be highly flexible, typically implying the existence of trade-offs between flexibility and performance or energy efficiency. Processor flexibility, however, is not very sharply defined, and consequently these claims cannot be validated, nor can such hypothetical relations be fully understood and exploited in the design of computing systems. This paper is an attempt to introduce scientific rigour to the notion of flexibility in computing systems. A survey is conducted to provide an overview of references to flexibility in literature, both in the computer architecture domain, as well as related fields. A classification is introduced to categorize different views on flexibility, which ultimately form the foundation for a qualitative definition of flexibility. Departing from the qualitative definition of flexibility, a generic quantifiable metric is proposed, enabling valid quantitative comparison of the flexibility of various architectures. To validate the proposed method, and evaluate the relation between the proposed metric and the general notion of flexibility, the flexibility metric is measured for 25 computing systems, including CPUs, GPUs, DSPs, and FPGAs, and 40 ASIPs taken from literature. The obtained results provide insights into some of the speculative trade-offs between flexibility and properties such as energy efficiency and area efficiency. Overall the proposed quantitative flexibility metric shows to be commensurate with some generally accepted qualitative notions of flexibility collected in the survey, although some surprising discrepancies can also be observed. The proposed metric and the obtained results are placed into context of the state of the art on compute flexibility, and extensive reflection provides not only a complete overview of the field, but also discusses possible alternative approaches and open issues. Note that this work does not aim to provide a final answer to the definition of flexibility, but rather provides a framework to initiate a broader discussion in the computer architecture society on defining, understanding, and ultimately taking advantage of flexibility.</p

Multimodal Image-to-Image Translation via a Single Generative Adversarial Network

Despite significant advances in image-to-image (I2I) translation with Generative Adversarial Networks (GANs) have been made, it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a pair of generator and discriminator. Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariant) features should be learned from images among all the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier, and shares the encoder and generator for all domains. As such, the SoloGAN model can be trained effectively with images from all domains such that the domain-invariant content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN model demonstrate the merits of the method, especially for the challenging I2I translation tasks, i.e., tasks that involve extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component using ablation studies.Comment: pages 13, 15 figure

An experimental study of clogging fault diagnosis in heat exchangers based on vibration signals

The water-circulating heat exchangers employed in petrochemical industrials have attracted great attentions in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions

Leaf transcriptome analysis of a subtropical evergreen broadleaf plant, wild oil-tea camellia (Camellia oleifera), revealing candidate genes for cold acclimation

Single nucleotide polymorphism (SNP) positions in genes of Camellia oleifera. Genotypes of samples from Jinggang (JG01-04) and Lu (LS01-04) mountains are shown. (XLSX 8324 kb

Using WeChat as an educational tool in MOOC-based flipped classroom: What can we learn from students’ learning experience?

Despite its importance, interaction remains limited in MOOC-based flipped classroom (MBFC) Grounded in social learning theory, we proposed an MBFC approach supported by social media to facilitate students’ interaction with peers and learning performance. A quasi-experiment was conducted to compare the MBFC approach (N = 58) based on WeChat with the conventional MBFC approach (N = 52). The results revealed that the use of WeChat in an MBFC approach led to better performance in terms of watching video lectures and completing online exercises before the class; however, it did not significantly enhance student learning performance compared to the conventional MBFC approach. In addition, the study found that students were moderately satisfied with the MBFC approach supported by WeChat. According to a WeChat interaction quantity and quality analysis, students’ non-substantive postings are much higher than students’ substantive postings in WeChat interaction groups, but students’ contributions to the postings have no significant effect on the final marks. Findings from this study could be of valuable reference for practitioners and researchers who plan to leverage social media tools such as WeChat to support student MOOC learning

Core-Shell Structure in Doped Inorganic Nanoparticles: Approaches for Optimizing Luminescence Properties

Doped inorganic luminescent nanoparticles (NPs) have been widely used in both research and application fields due to their distinctive properties. However, there is an urgent demand to improve their luminescence efficiency, which is greatly reduced by surface effects. In this paper, we review recent advances in optimizing luminescence properties of doped NPs based on core-shell structure, which are basically classified into two categories: one is by use of surface coating with nonmetal materials to weaken the influence of surface effect and the other is with metal shell via metal enhanced luminescence. Different materials used to coat NPs are surveyed, and their advantages and disadvantages are both commented on. Moreover, problems in current core-shell structured luminescent NPs are pointed out and strategies furthering the optimization of luminescence properties are suggested