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This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences, namely translating sequence to preference. To this end, we formulate the sequential recommendation task as a dictionary learning problem, which learns: 1) a shared dictionary matrix, each row of which represents a partial signal of user dynamic preferences shared across users; and 2) a posterior distribution estimator using a deep autoregressive model integrated with Gated Recurrent Unit (GRU), which can select related rows of the dictionary to represent a user's dynamic preferences conditioned on his/her past behaviors. Qualitative studies on the Netflix dataset demonstrate that the proposed method can capture the user preference drifts over time and quantitative studies on multiple real-world datasets demonstrate that the proposed method can achieve higher accuracy compared with state-of-the-art factorization and neural sequential recommendation methods. [ABSTRACT FROM AUTHOR]

We consider a class of nonconvex regularized optimization problems, which appear frequently in machine learning and data processing. Due to the structure of the problems, the iteratively reweighted algorithm was developed and applied to the consensus optimization. In this paper, we propose the acceleration of this scheme by adding an inertial term in each iteration. The proposed algorithms inherit the advantages of classical decentralized algorithms: they can be implemented over a connected network, in which the agents communicate with their neighbors and perform local computations. We also employ the diminishing stepsizes technique for the iteratively reweighted algorithm and consider its acceleration. In specific cases, our algorithms reduce to existing decentralized schemes and also indicate novel ones. Mathematically, we prove the convergence for both algorithms with several assumptions on the objective functions. With Kurdyka-Łojasiewicz property, convergence rates can be derived for constant stepsize case. Numerical results demonstrate the efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases. [ABSTRACT FROM AUTHOR]

Background: According to the guidelines, PD-L1 expression is a critical indicator for guiding immunotherapy application. According to certain studies, regardless of PD-L1 expression, immunotherapy could be advantageous for individuals with gastric cancer. Therefore, new scoring systems or biomarkers are required to enhance treatment strategies. Methods: Mass spectrometry and machine learning were used to search for strongly related PD-L1 genes, and the NMF approach was then used to separate gastric cancer patients into two categories. Differentially expressed genes (DEGs) between the two subtypes identified in this investigation were utilized to develop the UBscore predictive model, which was verified by the Gene Expression Omnibus (GEO) database. Coimmunoprecipitation, protein expression, and natural killing (NK) cell coculture experiments were conducted to validate the findings. Results: A total of 123 proteins were identified as PD-L1 interactors that are substantially enriched in the proteasome complex at the mRNA level. Using random forest, 30 UPS genes were discovered in the GSE66229 cohort, and ANAPC7 was experimentally verified as one of 123 PD-L1 interactors. Depending on the expression of PD-L1 and ANAPC7, patients were separated into two subgroups with vastly distinct immune infiltration. Low UBscore was related to increased tumor mutation burden (TMB) and microsatellite instability-high (MSI-H). In addition, chemotherapy medications were more effective in individuals with a low UBscore. Finally, we discovered that ANAPC7 might lead to the incidence of immunological escape when cocultured with NK-92 cells. Conclusion: According to our analysis of the PD-L1-related signature in GC, the UBscore played a crucial role in prognosis and had a strong relationship with TMB, MSI, and chemotherapeutic drug sensitivity. This research lays the groundwork for improving GC patient prognosis and treatment response. [ABSTRACT FROM AUTHOR]

• The dividing line of crack modes in the conventional RA-AF analysis method is difficult to determine. • The clustering algorithms successfully classified the crack modes of AE signals. • The dimensionality reduction results confirmed that AF has the greatest effect on crack classification. • The Gaussian mixture model is applicable to the crack classification of acoustic emission signals from different components. In actual projects, the damage of many critical components cannot be directly observed. Therefore, it is necessary to monitor their damage with structural health monitoring (SHM) technology to get the crack modes of the damage. Acoustic emission (AE) is a non-destructive testing (NDT) technique in structural health monitoring, and crack modes can be classified by analyzing the rise angle (RA) and average frequency (AF) of acoustic emission signals. However, the dividing line for classifying different crack patterns in this method is difficult to determine, and for the same member, different parameters can lead to a huge difference in the dividing line. This problem limits the application of the method. In this study, multiple machine learning algorithms were applied to cluster AE signals with known crack modes, and the clustering results were consistent with the real crack modes, solving the problem of difficult to determine the dividing line in the traditional RA-AF method. Furthermore, dimensionality reduction was performed on this set of AE signals, and the semi-empirical RA-AF analysis method was confirmed to be accurate. [ABSTRACT FROM AUTHOR]

Background: The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response. Methods: This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001. Results: For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized. Conclusion: The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction. Critical relevance statement: This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy. Key Points: This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction. [ABSTRACT FROM AUTHOR]

Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error. [ABSTRACT FROM AUTHOR]

Predicting material's microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material's microstructure hugely influences the material's properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina's microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains' shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve. [ABSTRACT FROM AUTHOR]

This paper revisits the temporal difference (TD) learning algorithm for the policy evaluation tasks in reinforcement learning. Typically, the performance of TD(0) and TD($\lambda$ λ ) is very sensitive to the choice of stepsizes. Oftentimes, TD(0) suffers from slow convergence. Motivated by the tight link between the TD(0) learning algorithm and the stochastic gradient methods, we develop a provably convergent adaptive projected variant of the TD(0) learning algorithm with linear function approximation that we term AdaTD(0). In contrast to the TD(0), AdaTD(0) is robust or less sensitive to the choice of stepsizes. Analytically, we establish that to reach an $\epsilon$ ε accuracy, the number of iterations needed is $\tilde{O}(\epsilon ^{-2}\ln ^4\frac{1}{\epsilon }/\ln ^4\frac{1}{\rho })$ O ˜ (ε - 2 ln 4 1 ε / ln 4 1 ρ) in the general case, where $\rho$ ρ represents the speed of the underlying Markov chain converges to the stationary distribution. This implies that the iteration complexity of AdaTD(0) is no worse than that of TD(0) in the worst case. When the stochastic semi-gradients are sparse, we provide theoretical acceleration of AdaTD(0). Going beyond TD(0), we develop an adaptive variant of TD($\lambda$ λ ), which is referred to as AdaTD($\lambda$ λ ). Empirically, we evaluate the performance of AdaTD(0) and AdaTD($\lambda$ λ ) on several standard reinforcement learning tasks, which demonstrate the effectiveness of our new approaches. [ABSTRACT FROM AUTHOR]

Semi-supervised learning has attracted significant attention in pattern recognition and machine learning. Among these methods, a very popular type is semi-supervised support vector machines. However, parameter selection in heat kernel function during the learning process is troublesome and harms the performance improvement of the hypothesis. To solve this problem, a novel local behavioral searching strategy is proposed for semi-supervised learning in this paper. In detail, based on human behavioral learning theory, the support vector machine is regularized with the un-normalized graph Laplacian. After building local distribution of feature space, local behavioral paradigm considers the form of the underlying probability distribution in the neighborhood of a point. Validation of the proposed method is performed with toy and real-life data sets. Results demonstrate that compared with traditional method, our method can more effectively and stably enhance the learning performance. [ABSTRACT FROM AUTHOR]

The insufficiency of labeled training data for representing the distribution of entire dataset is a major obstacle in various practical data mining applications. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, provide possibilities to solve this problem. Graph-based semi-supervised learning has recently become one of the most active research areas. In this paper, a novel graph-based semi-supervised learning approach entitled Class Dissimilarity based Linear Neighborhood Propagation (CD-LNP) is proposed, which assumes that each data point can be linearly reconstructed from its neighborhood. The neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. Our algorithm can propagate the labels from the labeled points to entire data set using these linear neighborhoods with sufficient smoothness. Experiment results demonstrate that our approach outperforms other popular graph-based semi-supervised learning methods. [ABSTRACT FROM AUTHOR]

In recent years, distributed stochastic algorithms have become increasingly useful in the field of machine learning. However, similar to traditional stochastic algorithms, they face a challenge where achieving high fitness on the training set does not necessarily result in good performance on the test set. To address this issue, we propose to use of a distributed network topology to improve the generalization ability of the algorithms. We specifically focus on the Sharpness-Aware Minimization (SAM) algorithm, which relies on perturbation weights to find the maximum point with better generalization ability. In this paper, we present the decentralized stochastic sharpness-aware minimization (D-SSAM) algorithm, which incorporates the distributed network topology. We also provide sublinear convergence results for non-convex targets, which is comparable to consequence of Decentralized Stochastic Gradient Descent (DSGD). Finally, we empirically demonstrate the effectiveness of these results in deep networks and discuss their relationship to the generalization behavior of SAM. [ABSTRACT FROM AUTHOR]

Review-based recommendation algorithms can alleviate the data sparsity issue in collaborative filtering by combining user ratings and reviews in model learning. However, most existing methods simplify the feature extraction process from reviews by assuming that different granularities of information (e.g., word, review, and feature) are equally important, which cannot optimally leverage the most important information and thus achieves suboptimal recommendation accuracy. Besides, many existing works directly regard text features as users or items representations, which may not be enough to make precise representations due to the large amount of redundant information in reviews. To tackle the two problems mentioned above, we propose a deep learning-based method named H ierarchical A ttention N etwork Oriented Towards C rowd I ntelligence (HANCI). First, HANCI replaces the commonly-used topic models or CNN text processor with an RNN text processor in review feature extraction, which can fully exploit the advantages of the sequential dependencies of reviews by using the whole hidden layers of the bidirectional LSTM as outputs. Second, HANCI weighs the importance of features guided by crowd intelligence to more accurately represent each user on each item, and vice versa. Third, HANCI utilizes a hierarchical attention network based on multi-level review text analysis to extract more precise user preferences and item latent features, so that HANCI can explore the importance of words, the usefulness of reviews and the importance of features to achieve more accurate recommendation. Extensive experiments on three public datasets show that HANCI outperforms the state-of-the-art review-based recommendation algorithms in accuracy and meanwhile provides insightful explanations. [ABSTRACT FROM AUTHOR]