A Parallel Framework for Streaming Dimensionality Reduction.

The visualization of streaming high-dimensional data often needs to consider the speed in dimensionality reduction algorithms, the quality of visualized data patterns, and the stability of view graphs that usually change over time with new data. Existing methods of streaming high-dimensional data visualization primarily line up essential modules in a serial manner and often face challenges in satisfying all these design considerations. In this research, we propose a novel parallel framework for streaming high-dimensional data visualization to achieve high data processing speed, high quality in data patterns, and good stability in visual presentations. This framework arranges all essential modules in parallel to mitigate the delays caused by module waiting in serial setups. In addition, to facilitate the parallel pipeline, we redesign these modules with a parametric non-linear embedding method for new data embedding, an incremental learning method for online embedding function updating, and a hybrid strategy for optimized embedding updating. We also improve the coordination mechanism among these modules. Our experiments show that our method has advantages in embedding speed, quality, and stability over other existing methods to visualize streaming high-dimensional data.

Interactive Visual Cluster Analysis by Contrastive Dimensionality Reduction.

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions.

Cohesively Enhancing the Conductance, Mechanical Robustness, and Environmental Stability of Random Metallic Mesh Electrodes via Hot-Pressing-Induced In-Plane Configuration.

Flexible transparent conductive electrode (FTCE) is highly desirable due to the fast-growing flexible optoelectronic devices. Several promising FTCEs based on metal material have been developed to replace conventional indium tin oxide (ITO). The random metal mesh is considered to be one of the competitive candidates. However, obtaining feasible random metal mesh with low sheet resistance, high transparency, good mechanical durability, and strong environmental stability is still a great challenge. Here, a random metal mesh-based FTCE with an in-plane structure, achieved by a facile hot-pressing process, is demonstrated. The hot-pressing process enables the fabrication of highly conductive FTCE with improved mechanical robustness and environmental stability. The in-plane FTCE shows a low sheet resistance of 1.63 Ω·sq-1 with an 80.6% transmittance, low relative resistance increase (RRI) of 7.9% after 240 h 85 °C/85% RH test, and low RRI of 8.0% after 105 cycles of bending test. Besides, various applications of the in-plane FTCE were demonstrated, including the flexible heater, flexible touch screen, and flexible electroluminescence. We anticipate that these results will spark interest in in-plane random metal mesh electrodes and enable the application of random metal mesh in flexible optoelectronic devices.

Flexible and Transparent Ferroferric Oxide-Modified Silver Nanowire Film for Efficient Electromagnetic Interference Shielding.

Transparent and flexible electromagnetic interference (EMI) shielding film is highly desirable due to the fast-growing flexible electronics. A silver nanowire (Ag NW) film is considered to be an ideal candidate for a transparent and flexible EMI shielding film but suffers low EMI shielding effectiveness (SE) at high transparency and poor bending durability. Herein, we introduce ferroferric oxide (Fe3O4) into a Ag NW film and demonstrate a robust EMI shielding film, which exhibits SE of 24.9 dB at 8.2 GHz and optical transparency of 90%. Fe3O4 exhibits roles of the improved absorption loss for electromagnetic radiation due to its high permeability, the enhanced reflection loss for electromagnetic radiation by increasing the conductivity of Ag NWs film, and the improved stability for the enhanced adhesion of the Ag NW EMI shielding film. Our work provides a facile method for high-performance transparent EMI shielding film, which exhibits great potential for protection for electronic devices.

Quantum Image Encryption Scheme Using Arnold Transform and S-box Scrambling.

The paper proposes a lossless quantum image encryption scheme based on substitution tables (S-box) scrambling, mutation operation and general Arnold transform with keys. First, the key generator builds upon the foundation of SHA-256 hash with plain-image and a random sequence. Its output value is used to yield initial conditions and parameters of the proposed image encryption scheme. Second, the permutation and gray-level encryption architecture is built by discrete Arnold map and quantum chaotic map. Before the permutation of Arnold transform, the pixel value is modified by quantum chaos sequence. In order to get high scrambling and randomness, S-box and mutation operation are exploited in gray-level encryption stage. The combination of linear transformation and nonlinear transformation ensures the complexity of the proposed scheme and avoids harmful periodicity. The simulation shows the cipher-image has a fairly uniform histogram, low correlation coefficients closed to 0, high information entropy closed to 8. The proposed cryptosystem provides 2256 key space and performs fast computational efficiency (speed = 11.920875 Mbit/s). Theoretical analyses and experimental results prove that the proposed scheme has strong resistance to various existing attacks and high level of security.