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ABSTRACT This paper studies the transfer learning problem for convolutional neural network models. A phase transition phenomenon has been empirically validated: The convolutional layer shifts from general to specific with respect to the target task as its depth increases. The paper suggests measuring the generality of convolutional layers through an easy‐to‐compute and tuning‐free statistic named projection correlation. The non‐asymptotic upper bounds for the estimation error of the proposed generality measure have been provided. Based on this generality measure, the paper proposes a forward‐adding‐layer‐selection algorithm to select general layers. The algorithm aims to find a cut‐off in the pre‐trained model according to where the phase transition from general to specific happens. Then, we propose to transfer only the general layers as specific layers can cause overfitting issues and hence hurt the prediction performance. The proposed algorithm is computationally efficient and can consistently estimate the true beginning of phase transition under mild conditions. Its superior empirical performance has been justified by various numerical experiments. 

Abstract This paper studies a tensor factor model that augments samples from multiple classes. The nuisance common patterns shared across classes are characterised by pervasive noises, and the patterns that distinguish different classes are represented by class‐specific components. Additionally, the pervasive component is modelled by the production of a low‐rank tensor latent factor and several factor loading matrices. This augmented tensor factor model can be expanded to a series of matrix variate tensor factor models and estimated using principal component analysis. The ranks of latent factors are estimated using a modified eigen‐ratio method. The proposed estimators have fast convergence rates and enjoy the blessing of dimensionality. The proposed factor model is applied to address the challenge of overlapping issues in image classification through a factor adjustment procedure. The procedure is shown to be powerful through synthetic experiments and an application to COVID‐19 pneumonia diagnosis from frontal chest X‐ray images. 

The estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This article addresses this limitation by focusing on precision matrix estimation for multivariate data characterized by a flexible yet unknown group structure. We introduce a novel approach that begins with the detection of this unknown group structure, clustering features within the low-dimensional space defined by the leading eigenvectors of the sample covariance matrix. Following this, we employ group-wise multivariate response linear regressions, guided by the identified group memberships, to estimate the precision matrix. We rigorously establish the theoretical foundations of our proposed method for both group detection and precision matrix estimation. The superior numerical performance of our approach is demonstrated through comprehensive simulation experiments and a comparative analysis with established methods in the field. Additionally, we apply our method to a real breast cancer dataset, showcasing its practical utility and effectiveness. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. 

In automated sleep monitoring systems, bed occupancy detection is the foundation or the first step before other downstream tasks, such as inferring sleep activities and vital signs. The existing methods do not generalize well to real-world environments due to single environment settings and rely on threshold-based approaches. Manually selecting thresholds requires observing a large amount of data and may not yield optimal results. In contrast, acquiring extensive labeled sensory data poses significant challenges regarding cost and time. Hence, developing models capable of generalizing across diverse environments with limited data is imperative. This paper introduces SeismoDot, which consists of a self-supervised learning module and a spectral-temporal feature fusion module for bed occupancy detection. Unlike conventional methods that require separate pre-training and fine-tuning, our self-supervised learning module is co-optimized with the primary target task, which directs learned representations toward a task-relevant embedding space while expanding the feature space. The proposed feature fusion module enables the simultaneous exploitation of temporal and spectral features, enhancing the diversity of information from both domains. By combining these techniques, SeismoDot expands the diversity of embedding space for both the temporal and spectral domains to enhance its generalizability across different environments. SeismoDot not only achieves high accuracy (98.49%) and F1 scores (98.08%) across 13 diverse environments, but it also maintains high performance (97.01% accuracy and 96.54% F1 score) even when trained with just 20% (4 days) of the total data. This demonstrates its exceptional ability to generalize across various environmental settings, even with limited data availability. 

In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are the Association for the Advancement of Medical Instrumentation (AAMI) and Food and Drug Administration (FDA), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field. 

Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features, but the relevant works for structural change detection, such as profile analysis for piecewise constant coefficients and integration analysis with multiple data sources, are limited. In this paper, we propose a generalized knockoff procedure (GKnockoff) for FDR control under such problem settings. We prove that the GKnockoff possesses pairwise exchangeability, and is capable of controlling the exact FDR under finite sample sizes. We further explore GKnockoff under high dimensionality, by first introducing a new screening method to filter the high-dimensional potential structural changes. We adopt a data splitting technique to first reduce the dimensionality via screening and then conduct GKnockoff on the refined selection set. Furthermore, the powers of proposed methods are systematically studied. Numerical comparisons with other methods show the superior performance of GKnockoff, in terms of both FDR control and power. We also implement the proposed methods to analyze a macroeconomic dataset for detecting changes of driven effects of economic development on the secondary industry. 

In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover’s algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing.