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Order is one of the most important concepts to interpret various phenomena such as the emergence of turbulence and the life-evolution process. The generation of laser can also be treated as an ordering process in which the interaction between the laser beam and the gain medium leads to the correlation between photons in the output optical field. Here, we demonstrate experimentally in a hybrid Raman-laser-optomechanical system that an ordered Raman laser can be generated from an entropy-absorption process by a chaotic optomechanical resonator. When the optomechanical resonator is chaotic or disordered enough, the Raman-laser field is in an ordered lasing mode. This can be interpreted by the entropy transfer from the Raman-laser mode to the chaotic motion mediated by optomechanics. Different order parameters, such as the box-counting dimension, the maximal Lyapunov exponent, and the Kolmogorov entropy, are introduced to quantitatively analyze this entropy transfer process, by which we can observe the order transfer between the Raman-laser mode and the optomechanical resonator. Our study presents a new mechanism of laser generation and opens up new dimensions of research such as the modulation of laser by optomechanics.

 

One key challenge encountered in single-cell data clustering is to combine clustering results of data sets acquired from multiple sources. We propose to represent the clustering result of each data set by a Gaussian mixture model (GMM) and produce an integrated result based on the notion of Wasserstein barycenter. However, the precise barycenter of GMMs, a distribution on the same sample space, is computationally infeasible to solve. Importantly, the barycenter of GMMs may not be a GMM containing a reasonable number of components. We thus propose to use the minimized aggregated Wasserstein (MAW) distance to approximate the Wasserstein metric and develop a new algorithm for computing the barycenter of GMMs under MAW. Recent theoretical advances further justify using the MAW distance as an approximation for the Wasserstein metric between GMMs. We also prove that the MAW barycenter of GMMs has the same expectation as the Wasserstein barycenter. Our proposed algorithm for clustering integration scales well with the data dimension and the number of mixture components, with complexity independent of data size. We demonstrate that the new method achieves better clustering results on several single-cell RNA-seq data sets than some other popular methods.

 

Abstract Plasmonic structural color, in which vivid colors are generated via resonant nanostructures made of common plasmonic materials, such as noble metals have fueled worldwide interest in backlight-free displays. However, plasmonic colors that were withstanding ultrahigh temperatures without damage remain an unmet challenge due to the low melting point of noble metals. Here, we report the refractory hafnium nitride (HfN) plasmonic crystals that can generate full-visible color with a high image resolution of ∼63,500 dpi while withstanding a high temperature (900 °C). Plasmonic colors that reflect visible light could be attributed to the unique features in plasmonic HfN, a high bulk plasmon frequency of 3.1 eV, whichcould support localized surface plasmon resonance (LSPR) in the visible range. By tuning the wavelength of the LSPR, the reflective optical response can be controlled to generate the colors from blue to red across a wide gamut. The novel refractory plasmonic colors pave the way for emerging applications ranging from reflective displays to solar energy harvesting systems. 

We present a novel multi-source uncertainty prediction approach that enables deep learning (DL) models to be actively trained with much less labeled data. By leveraging the second-order uncertainty representation provided by subjective logic (SL), we conduct evidence-based theoretical analysis and formally decompose the predicted entropy over multiple classes into two distinct sources of uncertainty: vacuity and dissonance, caused by lack of evidence and conflict of strong evidence, respectively. The evidence based entropy decomposition provides deeper insights on the nature of uncertainty, which can help effectively explore a large and high-dimensional unlabeled data space. We develop a novel loss function that augments DL based evidence prediction with uncertainty anchor sample identification. The accurately estimated multiple sources of uncertainty are systematically integrated and dynamically balanced using a data sampling function for label-efficient active deep learning (ADL). Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed ADL model. 

The rapid increase in both quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development; proper model interpretation; and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.