With the emergence of information overload in the Social Internet of Things (SIoT), personalized recommender systems have become essential for helping users locate the items they need. Effectively modeling the heterogeneous relationships across multiple information sources and weighting them according to their varying importance, especially under sparse social interactions in the SIoT, remains a key challenge. To address this issue, this paper proposes a Multi-source Adaptive Relational Graph Convolutional Network (MARGCN) framework for recommender systems. Useruser and useritem interactions are modeled as two relational graphs, and their embedded features are aggregated using graph convolutional networks (GCNs). A multi-source relationship perception model is designed to dynamically perceive and measure the importance of multiple relationships within the graph structure, thereby enhancing the ability to recognize heterogeneous information. An adaptive information fusion model is then constructed to dynamically integrate representations from different sources through a learnable fusion strategy, avoiding information loss or redundancy caused by simple weighting. Users and items are ultimately represented by aggregating and updating their embeddings via GCNs. Experiments show that compared with the most advanced methods, MARGCN improves the hit rate (HR) and the normalized discounted cumulative gain (NDCG) by 2.66%, 0.52%, and 3.93% in HR@5, HR@10, and HR@15 and by 1.58%, 1.32%, and 3.14% in NDCG@5, NDCG@10, and NDCG@15, respectively.

We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel trends assumption. We propose a novel doubly robust meta-learner for the Conditional Average Treatment Effect on the Treated (CATT), reducing the estimation to a convex risk minimization problem involving a set of auxiliary models. Our framework allows for the flexible estimation of the CATT, when conditioning on any subset of variables of interest using generic machine learning. Leveraging Neyman orthogonality, our proposed approach is robust to estimation errors in the auxiliary models. As a generalization to our main result, we develop a meta-learning approach for the estimation of general conditional functionals under covariate shift. We also provide an extension to the instrumented DiD setting with non-compliance. Empirical results demonstrate the superiority of our approach over existing baselines.

Accurate estimation of conditional average treatment effects (CATE) is at the core of personalized decision making. While there is a plethora of models for CATE estimation, model selection is a non-trivial task, due to the fundamental problem of causal inference. Recent empirical work provides evidence in favor of proxy loss metrics with double robust properties and in favor of model ensembling. However, theoretical understanding is lacking. Direct application of prior theoretical works leads to suboptimal oracle model selection rates due to the non-convexity of the model selection problem. We provide regret rates for the major existing CATE ensembling approaches and propose a new CATE model ensembling approach based on $Q$-aggregation using the doubly robust loss. Our main result shows that causal $Q$-aggregation achieves statistically optimal oracle model selection regret rates of $\log(M)/n$ (with $M$ models and $n$ samples), with the addition of higher-order estimation error terms related to products of errors in the nuisance functions. Crucially, our regret rate does not require that any of the candidate CATE models be close to the truth. We validate our new method on many semi-synthetic datasets and also provide extensions of our work to CATE model selection with instrumental variables and unobserved confounding.

In this paper, we propose a convolutional neural network (CNN) filter for reference picture resampling (RPR)-based super-resolution (SR) with wavelet decomposition. The proposed CNN filter takes the low resolution (LR) reconstructed frame (RecLR), LR prediction frame (PreLR) and high resolution (HR) RPR upsampled frame (RPRoutput) as the input for RPR-based SR. Thus, the proposed CNN filter not only learns a mapping function between LR and HR images, but also effectively removes blocking artifacts in the reconstructed frame. We adopt wavelet decomposition to make RPRoutputthe same size as RecLRand PreLRas well as obtain the relationship between high frequency (HF) and low frequency (LF) components. To maximize feature reuse under the limited parameters, we design a residual spatial and channel attention block (RSCB) that combines residual blocks with spatial attention and channel attention to learn the weighted local information and global information in different receptive fields. Experimental results show that the proposed CNN filter achieves -8.98% and -4.05% BD-rate reductions on Y channel in AI and RA configurations over VTM-11.0_NNVC-2.0 anchor, respectively.

Multi-sensor data has significant advantages over single sensor data due to the unique property of each sensor. We discover that multi-sensor collaboration can save bits and achieve stable compression performance according to quantization parameter (QP). In this paper, we propose a multi-sensor collaboration network for video compression based on wavelet decomposition, called MSCN. We introduce MSCN into 3D video coding based on color and depth sensors. The images acquired by a color sensor represent color and texture of the scene, while the images obtained by a depth sensor represent 3D geometric shape of the scene objects. Two sensor data are complementary, and color images help to reconstruct their corresponding depth images. First, we perform uniform sampling on the input depth video. Then, we compress the color and downsampled depth videos using 3D-HEVC codec. Finally, we reconstruct the depth video from the decoded color and depth videos by color guided depth super-resolution (SR). Experimental results show that MSCN achieves average BD-rate reductions of {−9.3%, −65.6%, −66.3%} and {−6.2%, −67.7%, and −69.2%} on 3D-HEVC test datasets for sampling factors 1, 2 and 4 in Random Access (RA) and All Intra (AI) configurations, respectively. Moreover, they verify that multi-sensor collaboration remarkably saves bits in video compression.

The hidden Markov model (HMM) is a broadly applied generative model for representing time-series data, and clustering HMMs attract increased interest from machine learning researchers. However, the number of clusters ( K ) and the number of hidden states ( S ) for cluster centers are still difficult to determine. In this article, we propose a novel HMM-based clustering algorithm, the variational Bayesian hierarchical EM algorithm, which clusters HMMs through their densities and priors and simultaneously learns posteriors for the novel HMM cluster centers that compactly represent the structure of each cluster. The numbers K and S are automatically determined in two ways. First, we place a prior on the pair (K,S) and approximate their posterior probabilities, from which the values with the maximum posterior are selected. Second, some clusters and states are pruned out implicitly when no data samples are assigned to them, thereby leading to automatic selection of the model complexity. Experiments on synthetic and real data demonstrate that our algorithm performs better than using model selection techniques with maximum likelihood estimation.

Hierarchical learning of generative models is useful for representing and interpreting complex data. For instance, one application is to learn an HMM to represent an individual’s eye fixations on a stimuli, and then cluster individuals’ HMMs to discover common eye gaze strategies. However, learning the individual representation models from observations and clustering individual models to group models are often considered as two separate tasks. In this paper, we propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children. Due to the regularization process, our method has advantages when the number of training samples decreases. Experimental results on the synthetic datasets demonstrate the effectiveness of the proposed method.

BACKGROUND: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. RESULTS: Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. CONCLUSION: Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

In the past few years, many methods were proposed to model the so-called secondary path on line for an active noise control (ANC) system. In these methods, an auxiliary noise is employed to excite the modeling process. Among many factors to affect the performance of these methods, some play important roles. They are: 1) power of auxiliary noise; 2) ability to detect secondary path change; 3) optimal step sizes for adaptive filters used in an ANC system and 4) independence between ANC and secondary path modeling processes. In literature, however, there is lack of analysis to show how these factors affect the performance of the ANC system. Moreover, the aforementioned online secondary path modeling methods have not been systematically evaluated. In this paper, the performance of these methods is evaluated by investigating the above-mentioned factors through both statistical analysis and computer simulations.

In many practical cases for active noise control (ANC), the online secondary path modeling methods that use auxiliary noise are often applied. However, the auxiliary noise contributes to residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, a sudden and large change in the secondary path leads to easy divergence of the existing online secondary path modeling methods. To mitigate these problems, this paper proposes a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation. The auxiliary noise power is scheduled based on the convergence status of an ANC system with consideration of the variation of the primary noise. The purpose is to alleviate the increment of the residual noise due to the auxiliary noise. In addition, the norm manipulation is applied to adaptive filters in the ANC system. The objective is to avoid over-updates of adaptive filters due to the sudden large change in the secondary path and thus prevent the ANC system from diverging. Computer simulations show the effectiveness and robustness of the proposed method.

A weight-constrained filtered-x LMS (CFxLMS) algorithm is proposed for broadband feedforward active noise control system. Compared with the system using the normal FxLMS algorithm, the proposed system is more robust against interference to error microphone. Computer simulation results show the effectiveness of the algorithm.

In an active noise control (ANC) system using the filtered-x least mean square (FxLMS) algorithm, an online secondary path modeling method that uses an injected auxiliary noise is often applied. Such a method allows quick and full-band signal-independent modeling. In addition, it is suitable for multisecondary path modeling. Normally, the larger the auxiliary noise, the faster an accurate model can be obtained. However, it increases the residual noise of the ANC system. To mitigate this problem, in this letter, a new online secondary path modeling method is proposed. Rather than fixed, the power of auxiliary noise is varied according to the working status of the ANC system. More specifically, the auxiliary noise is large before the ANC system converges, and becomes small when the system converges. Computer simulations show its effectiveness and robustness.

A good active noise control (ANC) system with online secondary path modeling should have the property that the operation of the ANC controller and the modeling of the secondary path are mutually independent. A new ANC system with online secondary path modeling is presented. Three cross-updated least mean square (LMS) adaptive filters are used to reduce mutual disturbances between the operation of the ANC controller and the modeling of the secondary path. Computer simulations have been conducted and the results show that the proposed method is able to produce superior performance compared to existing methods.

This paper describes a low bit rate standalone portable video communication system developed for wireless applications. The video codec is based on H.263, and the audio codec is based on G.723.1. The transmission bandwidth is limited to 25 kHz. In order to have reasonable good video and audio quality, the source bit rate is set to 38 kbps. This includes video, audio and synchronization. The bit stream is embedded with 26 kbps for the error protection, resulted in a total of 64 kbps. A 16-QAM is used for the modulation in order to meet the bandwidth requirement. The whole system deployed three general purpose DSP for the real time implementation of video coding, audio coding, channel coding and system control. Besides, an RF module is developed for the transmission on UHF band.

A method to improve the performance of the secondary path modeling for active noise control systems is proposed in this letter. The proposed method can further reduce the distortion introduced by the primary noise and the canceling signal to the secondary path modeling. Computer simulation shows that the proposed method can provide faster convergence and higher modeling accuracy.

This paper gives a description of our real-time software MPEG-4 video decoder. The performance of the decoder and the fast algorithms used for real-time decoding are discussed. Our techniques used to speed up four computationally intensive modules, inverse discrete cosine transform, context-based arithmetic decoding, padding, and variable length decoding, are also presented in this paper. Our decoding speed is improved so that software real-time video decoding is possible.