Dynamic Semiparametric Factor Model with a Common Break

For change-point analysis of high dimensional time series, we consider a semiparametric model with dynamic structural break factors. The observations are described by a few low dimensional factors with time-invariate loading functions of covariates. The unknown structural break in time models the regime switching e ects introduced by exogenous shocks. In particular, the factors are assumed to be nonstationary and follow a Vector Autoregression (VAR) process with a structural break. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic convergence results for making inference on the common breakpoint in time. The estimation precision is evaluated via a simulation study. Finally we present two empirical illustrations on modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset

Combining the Silhouette and Skeleton Data for Gait Recognition

Gait recognition, a promising long-distance biometric technology, has aroused intense interest in computer vision. Existing works on gait recognition can be divided into appearance-based methods and model-based methods, which extract features from silhouettes and skeleton data, respectively. However, since appearance-based methods are greatly affected by clothing changing and carrying condition, and model-based methods are limited by the accuracy of pose estimation approaches, gait recognition remains challenging in practical applications. In order to integrate the advantages of such two approaches, a two-branch neural network (NN) is proposed in this paper. Our method contains two branches, namely a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. In addition, two new modules are proposed in the GCN-based branch for better gait representation. First, we present a simple yet effective fully connected graph convolution operator to integrate the multi-scale graph convolutions and alleviate the dependence on natural human joint connections. Second, we deploy a multi-dimension attention module named STC-Att to learn spatial, temporal and channel-wise attention simultaneously. We evaluated the proposed two-branch neural network on the CASIA-B dataset. The experimental results show that our method achieves state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image Understandin

Simultaneous Inference of a Partially Linear Model in Time Series

We introduce a new methodology to conduct simultaneous inference of the nonparametric component in partially linear time series regression models where the nonparametric part is a multivariate unknown function. In particular, we construct a simultaneous confidence region (SCR) for the multivariate function by extending the high-dimensional Gaussian approximation to dependent processes with continuous index sets. Our results allow for a more general dependence structure compared to previous works and are widely applicable to a variety of linear and nonlinear autoregressive processes. We demonstrate the validity of our proposed methodology by examining the finite-sample performance in the simulation study. Finally, an application in time series, the forward premium regression, is presented, where we construct the SCR for the foreign exchange risk premium from the exchange rate and macroeconomic data.Comment: 61 pages, 6 figure

ℓ2\ell^2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM

We propose an inference method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. Our method aggregates moving sum (MOSUM) statistics cross-sectionally by an $\ell^2$-norm and maximizes them over time. We further introduce a novel Two-Way MOSUM, which utilizes spatial-temporal moving regions to search for breaks, with the added advantage of enhancing testing power when breaks occur in only a few groups. The limiting distribution of an $\ell^2$-aggregated statistic is established for testing break existence by extending a high-dimensional Gaussian approximation theorem to spatial-temporal non-stationary processes. Simulation studies exhibit promising performance of our test in detecting non-sparse weak signals. Two applications, analyzing equity returns and COVID-19 cases in the United States, showcase the real-world relevance of our proposed algorithms.Comment: 111 pages, 10 figure

High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice

Table S4. Annotated function of differentially expressed genes identified between parents. (XLSX 1232 kb

Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.Comment: submitted to IEEE ICASSP 202

Dynamic Semiparametric Factor Model with Structural Breaks

For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression (VAR) process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. {Importantly, our results hold for both large and small breaks in the factor dependency structure.} The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset

Electronic Post-Compensation of Optical Transmission Impairments Using Digital Backward Propagation

Systems and method of compensating for transmission impairment are disclosed. One such method comprises: receiving an optical signal which has been distorted in the physical domain by an optical transmission channel; and propagating the distorted optical signal backward in the electronic domain in a corresponding virtual optical transmission channel