Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a significant computational burden. This motivates us to consider finding expressions of the posterior distribution that are computationally straightforward to obtain independent replicates from directly. We focus on a broad class of Bayesian latent Gaussian process (LGP) models that allow for spatially dependent data. First, we derive a new class of distributions we refer to as the generalized conjugate multivariate (GCM) distribution. The GCM distribution's theoretical development is similar to that of the CM distribution with two main differences; namely, (1) the GCM allows for latent Gaussian process assumptions, and (2) the GCM explicitly accounts for hyperparameters through marginalization. The development of GCM is needed to obtain independent replicates directly from the exact posterior distribution, which has an efficient projection/regression form. Hence, we refer to our method as Exact Posterior Regression (EPR). Illustrative examples are provided including simulation studies for weakly stationary spatial processes and spatial basis function expansions. An additional analysis of poverty incidence data from the U.S. Census Bureau's American Community Survey (ACS) using a conditional autoregressive model is presented.

Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Latent Gaussian Process Models

The proliferation of mobile devices has led to the collection of large amounts of population data. This situation has prompted the need to utilize this rich, multidimensional data in practical applications. In response to this trend, we have integrated functional data analysis (FDA) and factor analysis to address the challenge of predicting hourly population changes across various districts in Tokyo. Specifically, by assuming a Gaussian process, we avoided the large covariance matrix parameters of the multivariate normal distribution. In addition, the data were both time and spatially dependent between districts. To capture these characteristics, a Bayesian factor model was introduced, which modeled the time series of a small number of common factors and expressed the spatial structure through factor loading matrices. Furthermore, the factor loading matrices were made identifiable and sparse to ensure the interpretability of the model. We also proposed a Bayesian shrinkage method as a systematic approach for factor selection. Through numerical experiments and data analysis, we investigated the predictive accuracy and interpretability of our proposed method. We concluded that the flexibility of the method allows for the incorporation of additional time series features, thereby improving its accuracy.

Spatiotemporal factor models for functional data with application to population map forecast

Abstract. A photodetector that utilizes the phase-changing material Ge2Sb2Te5 (GST) integrated with a silicon on insulator (SOI) waveguide is designed and analyzed in the wavelength (λ) range of 1500 to 1600 nm for telecommunication and optical fiber communication. The analysis of simulated structure reveal that GST is a promising material for photodetection applications due to phase-change property on a nanoscale, excellent absorption (with an absorption coefficient of ∼106  cm  −  1), lower bandgap of 0.63 eV in the amorphous phase, and 0.53 eV in the crystalline phase, tunability, and high phase stability. The photodetector structure consists of an SOI substrate, silicon waveguide, GST layer as phase changing material, and gold (Au) to make contacts. The photodetector’s optical and electrical parameters, such as electric field distribution of carrier generation rate (G), absorbed power (Pabs), dark current (Idark), photocurrent (Iph), and responsivity (R), are analyzed for both the amorphous (a-GST) and crystalline (c-GST) phases. The results demonstrate the potential of GST material to design high-performance photodetectors for various applications. The highest resistivity of 2.29  A  /  W is achieved for a-GST at λ  =  1550  nm, with a bias voltage range of 0.5 to 2.5 V. The maximum absorption is observed in the amorphous phase as compared to the crystalline phase of GST. The calculated values of Iph and Idark are 0.24 mA and 78.54  μA for a-GST and 0.12 mA and 15.71  μA for c-GST, respectively. The optoelectrical behavior of GST-based photodetector on SOI waveguide demonstrates their potential application in photonic integrated circuits. Our work proposes an approach for using GST in photodetectors and analyzes both phases of GST that have not been explored before. Therefore, for the viability of the reported work, a detailed comparison is also presented with other simulated structures and found that the proposed device provided excellent results with low bias voltage.

Simulation of waveguide integrated Ge2Sb2Te5-based tunable photodetector

The coronavirus (COVID-19) global pandemic has made a signiﬁcant impact on people’s social activities. Cell phone mobility data provide unique and rich information on studying this impact. The motivating dataset of this study is the daily leaving-home index data at Harris County in Texas provided by SafeGraph. To study changes in daily leaving-home index and how they relate to public policy and socio-demographic variables, we propose a new Bayesian wavelet model for modeling and clustering spatial functional data, where domain partitioning is achieved by operating on the spanning trees. The resulting clusters can have arbitrary shapes and are spatially contiguous in the input domain. An efﬁcient tailored reversible jump Markov chain Monte Carlo algorithm is proposed to implement the model. The method is applied to the spatial functional data of the daily percentages of people who left home. We focus on the time period covering both lock-down and phased re-opening in Texas during the COVID-19 pandemic, and study the changing behaviors of those functional curves. By linking the clustering results with the socio-demographic information, we identify several covariates of census blocks that have a noticeable impact on the clustering patterns of people’s mobility behaviors.

Bayesian clustering of spatial functional data with application to a human mobility study during covid-19

: A programmable hardware implementation of all-optical nonlinear activation functions for different scenarios and applications in all-optical neural networks is essential. We demonstrate a programmable, low-loss all-optical activation function device based on a silicon micro-ring resonator loaded with phase change materials. Four different nonlinear activation functions of Relu, ELU, Softplus and radial basis functions are implemented for incident signal light of the same wavelength. The maximum power consumption required to switch between the four different nonlinear activation functions in calculation is only 1.748 nJ. The simulation of classification of hand-written digit images also shows that they can perform well as alternative nonlinear activation functions. The device we design can serve as nonlinear units in photonic neural networks, while its nonlinear transfer function can be flexibly programmed to optimize the performance of different neuromorphic tasks.

Programmable low-power consumption all-optical nonlinear activation functions using a micro-ring resonator with phase-change materials

The use of optical carrier frequencies will enable seamless data connection for future terrestrial and underwater internet uses and will resolve the technological gap faced by other communication modalities. However, several issues must be solved to propel this technological shift, which include the limitations in designing optical receivers with large detection areas, omnidirectionality, and high modulation bandwidth, mimicking antennas operating in the radio-frequency spectrum. To address this technological gap, herein, we demonstrate halideperovskite-polymer–based scintillating fibers as a near-omnidirectional detection platform for several tens-to-hundreds of Mbit/s optical communication in both free space and underwater links. The incorporation of all-inorganic CsPbBr3 nanocrystals by engineering the nanocrystal concentration in an ultraviolet-curable polymer matrix ensures a high photoluminescence quantum yield, Mega-Hertz modulation bandwidth and Mbit/s data rate suitable to be used as a highspeed fibers-based receiver. The resultant perovskite polymer-based scintillating fibers offer flexibility in terms of shape and near-omnidirectional detection features. Such fiber properties also introduce a scalable detection area which can resolve the resistance-capacitance and angleof-acceptance limits in planar-based detectors, which conventionally impose a trade-off between the modulation bandwidth, detection area, and angle of view. A high bit rate of 23 Mbit/s and 152.5 Mbit/s was achieved using an intensity-modulated laser for non-return-to-zero on-offkeying (NRZ-OOK) modulation scheme in free-space and quadrature amplitude modulation orthogonal frequency-division multiplexing (QAM-OFDM) modulation scheme in an underwater environment, respectively. Our near-omnidirectional optical-based antenna based on perovskitepolymer-based scintillating fibers sheds light on the immense possibilities of incorporating functional nanomaterials for empowering light-based terrestrialand underwater-internet systems. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

All-inorganic halide-perovskite polymer-fiber-photodetector for high-speed optical wireless communication

The two-sided markets such as ride-sharing companies often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development of smart phones and internet of things, they have substantially transformed the transportation landscape of human beings. In this paper we consider large-scale ﬂeet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time. Major technical challenges, such as policy evaluation, arise in those studies because (i) spatial and temporal proximities induce interference between locations and times; and (ii) the large number of locations results in the curse of dimensionality. To address both challenges simultaneously, we introduce a multi-agent reinforcement learning (MARL) framework for carrying policy evaluation in these studies. We propose novel estimators for mean outcomes under different products that are consistent despite the high-dimensionality of state-action space. The proposed estimator works favorably in simulation experiments. We further illustrate our method using a real dataset obtained from a two-sided marketplace company to evaluate the effects of applying different subsidizing policies. A Python implementation of the proposed method is available at https://github.com/RunzheStat/CausalMARL .

A multi-agent reinforcement learning framework for off-policy evaluation in two-sided markets

Stripe artifacts can deteriorate the quality of light sheet fluorescence microscopy (LSFM) images. Owing to the inhomogeneous, high-absorption, or scattering objects located in the excitation light path, stripe artifacts are generated in LSFM images in various directions and types, such as horizontal, anisotropic, or multidirectional anisotropic. These artifacts severely degrade the quality of LSFM images. To address this issue, we proposed a new deep-learningbased approach for the elimination of stripe artifacts. This method utilizes an encoder–decoder structure of UNet integrated with residual blocks and attention modules between successive convolutional layers. Our attention module was implemented in the residual blocks to learn useful features and suppress the residual features. The proposed network was trained and validated by generating three different degradation datasets with different types of stripe artifacts in LSFM images. Our method can effectively remove different stripes in generated and actual LSFM images distorted by stripe artifacts. Besides, quantitative analysis and extensive comparison results demonstrated that our method performs the best compared with classical image-based processing algorithms and other powerful deep-learning-based destriping methods for all three generated datasets. Thus, our method has tremendous application prospects to LSFM, and its use can be easily extended to images reconstructed by other modalities affected by the presence of stripe artifacts. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network

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