On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states

Deformable Object Tracking with Gated Fusion

The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. Extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against state-of-the-art methods

Life cycle evolution in the trilobites Balangia and Duyunaspis from the Cambrian Series 2 (Stage 4) of South China

The evolution process can be reconstructed by tracking the changes in the dynamic characters of life cycles. A number of related trilobites from the Cambrian of South China provide additional information for the study of trilobite evolutionary patterns, which has been hampered by previous incomplete fossil record though. Here, Balangia and Duyunaspis represent related Cambrian oryctocephalid trilobites from South China, are comprehensively discussed over the ontogeny, and the results show that, from B. balangensis via D. duyunensis to D. jianheensis, their exoskeletal morphology shows a directional evolution. Based on the direction of evolutionary changes in the development of Balangia and Duyunaspis, we speculate that Duyunaspis likely evolved from Balangia instead of Balangia evolved from Duyunaspis, as was previously assumed. This inference is also supported by the phylogenetic tree. This research provides not only a better understanding of the mechanisms of evolution in trilobites, but also new insights for the relationship between developmental evolutionary changes and phylogeny in trilobites.Depto. de Geodinámica, Estratigrafía y PaleontologíaFac. de Ciencias GeológicasTRUENational Natural Science Foundation of ChinaGuizhou Bureau of Science and TechnologyPriority Research Program of Chinese Academy of Sciencespu

Influences of the mixed LiCl-CaCl 2 liquid desiccant solution on a membrane-based dehumidification system: parametric analysis and mixing ratio selection

The membrane-based liquid desiccant dehumidification system has high energy efficiency without the traditional liquid system carry-over problem. The performance of such a system strongly depends on solution's temperature and concentration, which have direct relationship to the solution surface vapour pressure. Compared with the pure liquid desiccant solution, the mixed liquid desiccant solution has lower surface vapour pressure, better system performance and lower material cost. In this paper, the performance of a flat-plate membrane-based liquid desiccant dehumidification system with the mixed solution (LiCl and CaCl2) is investigated through theoretical and experimental approaches. A mathematical model is established to predict the system performance, while the electrolyte non-random two-liquid (NRTL) method is applied to calculate the mixed solution properties. The influences of the solution mixing ratio, temperature Tsol and concentration Csol are evaluated, and it is found that the regeneration heat Qreg can be dramatically reduced by either applying a high concentration solution or increasing CaCl2 content in the mixed solution. Compared with the pure LiCl solution system, the mixed solution system COP can be improved up to 30.23% by increasing CaCl2 content for a 30% concentration solution. The optimum mixing ratio varies with the solution concentration. For the mixed LiCl-CaCl2 solution, the system highest COPs appear at the mixing ratios of 3:1, 2:1 and 1:1 for 20%, 30% and 40% concentrations respectively

Closed-Loop and Robust Control of Quantum Systems

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention

State-of-the-art review of 3DPV technology: structures and models

© 2019 Elsevier Ltd Increasing energy conversion efficiency from sunlight to power is one of the key solutions for the world's energy shortage and greenhouse gas reduction, but the conventional flat photovoltaic module without sun tracking mechanism has the low sunlight energy collection ability. This paper presents the state-of-the-art three-dimensional photovoltaic (3DPV) technology with high photovoltaic energy conversion efficiency, which is able to absorb off-peak sunlight and reflected light more effectively, thereby it can generate more power. At first, this paper is to catalogue and critique different 3DPV structures and models, as well as assess their characteristics. Afterwards, the main influence factors on the 3DPV structures and models including shape, height and spacing of the solar cells, latitude of the installation, optimal device design and shadow cast, are reviewed. Finally, the challenges and future technological developments of 3DPV structures and models are highlighted. This study demonstrated that the 3DPV technology can increase the captured sunlight approximately 15–30% in comparison with the conventional flat PV technology

Genome-wide comparison of microRNAs and their targeted transcripts among leaf, flower and fruit of sweet orange

BACKGROUND: In plants, microRNAs (miRNAs) regulate gene expression mainly at the post-transcriptional level. Previous studies have demonstrated that miRNA-mediated gene silencing pathways play vital roles in plant development. Here, we used a high-throughput sequencing approach to characterize the miRNAs and their targeted transcripts in the leaf, flower and fruit of sweet orange. RESULTS: A total of 183 known miRNAs and 38 novel miRNAs were identified. An in-house script was used to identify all potential secondary siRNAs derived from miRNA-targeted transcripts using sRNA and degradome sequencing data. Genome mapping revealed that these miRNAs were evenly distributed across the genome with several small clusters, and 69 pre-miRNAs were co-localized with simple sequence repeats (SSRs). Noticeably, the loop size of pre-miR396c was influenced by the repeat number of CUU unit. The expression pattern of miRNAs among different tissues and developmental stages were further investigated by both qRT-PCR and RNA gel blotting. Interestingly, Csi-miR164 was highly expressed in fruit ripening stage, and was validated to target a NAC transcription factor. This study depicts a global picture of miRNAs and their target genes in the genome of sweet orange, and focused on the comparison among leaf, flower and fruit tissues. CONCLUSIONS: This study provides a global view of miRNAs and their target genes in different tissue of sweet orange, and focused on the identification of miRNA involved in the regulation of fruit ripening. The results of this study lay a foundation for unraveling key regulators of orange fruit development and ripening on post-transcriptional level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-695) contains supplementary material, which is available to authorized users