Scalable nonparametric multiway data analysis

Abstract Multiway data analysis deals with multiway arrays, i.e., tensors, and the goal is twofold: predicting missing entries by modeling the interactions between array elements and discovering hidden patterns, such as clusters or communities in each mode. Despite the success of existing tensor factorization approaches, they are either unable to capture nonlinear interactions, or computationally expensive to handle massive data. In addition, most of the existing methods lack a principled way to discover latent clusters, which is important for better understanding of the data. To address these issues, we propose a scalable nonparametric tensor decomposition model. It employs Dirichlet process mixture (DPM) prior to model the latent clusters; it uses local Gaussian processes (GPs) to capture nonlinear relationships and to improve scalability. An efficient online variational Bayes Expectation-Maximization algorithm is proposed to learn the model. Experiments on both synthetic and real-world data show that the proposed model is able to discover latent clusters with higher prediction accuracy than competitive methods. Furthermore, the proposed model obtains significantly better predictive performance than the state-of-the-art large scale tensor decomposition algorithm, GigaTensor, on two large datasets with billions of entries

Scaling of Berry-curvature monopole dominated large linear positive magnetoresistance

The linear positive magnetoresistance (LPMR) is a widely observed phenomenon in topological materials, which is promising for potential applications on topological spintronics. However, its mechanism remains ambiguous yet and the effect is thus uncontrollable. Here, we report a quantitative scaling model that correlates the LPMR with the Berry curvature, based on a ferromagnetic Weyl semimetal CoS2 that bears the largest LPMR of over 500% at 2 Kelvin and 9 Tesla, among known magnetic topological semimetals. In this system, masses of Weyl nodes existing near the Fermi level, revealed by theoretical calculations, serve as Berry-curvature monopoles and low-effective-mass carriers. Based on the Weyl picture, we propose a relation $$\text{MR}=\frac{e}{\hbar }B{{\Omega }_{\text{F}}}$$, with B being the applied magnetic field and $${{\Omega }_{\text{F}}}$$ the average Berry curvature near the Fermi surface, and further introduce temperature factor to both MR/B slope (MR per unit field) and anomalous Hall conductivity, which establishes the connection between the model and experimental measurements. A clear picture of the linearly slowing down of carriers, i.e., the LPMR effect, is demonstrated under the cooperation of the k-space Berry curvature and real-space magnetic field. Our study not only provides an experimental evidence of Berry curvature induced LPMR for the first time, but also promotes the common understanding and functional designing of the large Berry-curvature MR in topological Dirac/Weyl systems for magnetic sensing or information storage

Intensive variable and its application

Opening with intensive variables theory, using a combination of static and dynamic GIS and integrating numerical calculation and spatial optimization, this book creates a framework and methodology for evaluating land use effect, among other concepts

Engineering Phosphinate-Containing Rhodamines for Turn-On Photoacoustic Imaging Applications

Photoacoustic imaging (PAI) is an emerging imaging technique with applications in preclinical and point-of-care settings. PAI is a light-in, sound-out technique which uses pulsed laser excitation with near-infrared (NIR) light to elicit local temperature increases through non-radiative relaxation events, ultimately leading to the production of ultrasound waves. The classical xanthene dye scaffold has found numerous applications in fluorescence imaging, however, xanthenes are rarely utilized for PAI since they do not typically display NIR absorbance. Herein, we report the ability of Nebraska Red (NR) dyes to produce photoacoustic (PA) signal and provide a rational design approach to reduce the hydrolysis rate of ester containing dyes. By converting a relatively hydrolytically labile phosphinate ester to a more stable thiophosphinate ester, we were able to reduce the rate of ester hydrolysis 3.6-fold within a new dye, termed SNR700. Leveraging the stabilized NIR absorbance of this dye, we were able to construct the first rhodamine-based, turn-on PAI imaging probe for hypochlorous acid (HOCl) that is compatible with commercial PA instrumentation. This probe, termed SNR700-HOCl, has a limit of detection of 500 nM for HOCl and is capable of producing contrast up to 2.9 cm deep in tissues using PAI. This work provides a new set of rhodamine-based PAI agents as well as a rational design approach to stabilize esterified versions of NR dyes with desirable properties for PAI. In the long term, the reagents described herein could be utilized to enable non-invasive imaging of HOCl in disease-relevant model systems

Spatial and Temporal Characteristics of Road Networks and Urban Expansion

Urban expansion has become a widespread trend in developing countries. Road networks are an extremely important factor driving the expansion of urban land and require further study. To investigate the relationship between road networks and urban expansion, we selected Beijing, New York, London, and Chicago as study areas. First, we obtained urban land use vector data through image interpretation using a remote sensing (RS) and geographic information systems (GIS) platform and then used overlay analysis to extract information on urban expansion. A road network density map was generated using the density analysis tool. Finally, we conducted a spatial statistical analysis between road networks and urban expansion and then systematically analyzed their distribution features. In addition, the Urban Expansion-Road Network Density Model was established based on regression analysis. The results indicate that (1) the road network density thresholds of Beijing, New York, London, and Chicago are 18.9 km/km2, 37.8 km/km2, 57.0 km/km2, and 64.7 km/km2, respectively, and urban expansion has an inverted U-curve relationship with road networks when the road network density does not exceed the threshold; (2) the calculated turning points for urban expansion indicate that urban expansion initially accelerates with increasing road network density but then decreases after the turning point is reached; and (3) when the road density exceeds the threshold, urban areas cease to expand. The correlation between urban expansion and road network features provides an important reference for the future development of global cities. Understanding road network density offers some predictive capabilities for urban land expansion, facilitates the avoidance of irregular expansion, and provides new ideas for addressing the inefficient utilization of land

Urban Competitiveness Measurement of Chinese Cities Based on a Structural Equation Model

In the current era, competition among countries and regions is in fact among cities. Thus, how to measure urban competitiveness precisely is a basic and important question. The two main approaches to this are comprehensive evaluation based on a set of indicators and network analysis based on inter-city relations. However, both have shortcomings. In this study, we introduced structural equation model (SEM) into urban competitiveness measurement to integrate the two approaches. We built a partial least squares structural equation model (PLS–SEM) according to the analysis of causal relationship among urban attribute indicators → urban functions → urban competitiveness → urban flow intensities. Following the processes of algorithm selection, model building, fitting and assessment, and modification in PLS-SEM modeling, we measured the urban competitiveness of Chinese cities in 2010 and analyzed its distribution quantitatively and spatially. The results revealed relationships between factors contained in the model and urban competitiveness and proved that the PLS-SEM urban competitiveness measurement approach we proposed is theoretically reliable and statistically valid

A long-wavelength xanthene dye for photoacoustic imaging

Photoacoustic (PA) imaging is a powerful biomedical imaging modality. We designed KeTMR and KeJuR, two xanthene-based dyes that readily obtained through a 2-step synthetic route. KeJuR has low molecular weight, good aqueous solubility, and superior chemical stability compared to KeTMR. KeJuR shows robust PA signal at 860 nm excitation and can be paired with traditional PA dyes for multiplex imaging in blood samples under tissue-mimicking environment

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment