Identifying influential nodes in complex networks poses a significant challenge, requiring a delicate balance among efficiency, accuracy, and redundancy elimination. To address this, we propose TIG-IM (A Two-stage Inductive GNN for Influence Maximization), a novel framework based on GNN. In Stage 1, we construct a multi-task learning-based global scorer. This scorer utilizes a Graph Attention Network (GAT) to simultaneously regress the SIR propagation score and our newly proposed 3D-Bridging Centrality. This multi-task design enables efficient identification of cross-community bridge nodes to generate a high-quality candidate pool. In Stage 2, we design a local selector that introduces a composite loss function with sparsity and diversity regularization. This selector performs a secondary re ranking on the candidate subgraph to produce a final seed set that is both structurally dispersed and possesses complementary propagation potential. Theoretical analysis proves that our scorer satisfies monotonicity and that the local loss can be interpreted as a MAP estimation. Extensive experiments on twelve real-world networks from diverse domains, including social, academic, and biological, demonstrate that TIG-IM significantly outperforms various state-of-the-art baselines in both spreading effectiveness and computational efficiency. Ablation studies further validate the synergistic value of our three core components: 3D-Bridging Centrality, sparsity penalty, and diversity-based re-ranking.

Precise estimation of correlation or similarity between two random variables lies at the heart of signal detection, target localization and pattern recognition. In this paper, we show that there exists a large class of multiplier-less analog correlators that can demonstrate a higher signal-to-noise ratio (SNR) compared to a conventional multiply-accumulate (MAC) based correlator. The multiplier-less design uses a Margin Propagation (MP) principle combining rectifying diodes in a symmetric circuit architecture. Using Price’s theorem we present a novel analytical framework that can be used to understand the steady-state behavioral response of different MP correlator circuits. The analytical results have been verified using transient and steady-state circuit simulations of correlator circuits designed in a standard CMOS process.

It has always been a tricky task to generate image hashing function via deep learning without labels and allocate the relative distance between data through their features. Existing methods can complete this task and prevent the overfitting problem using shallow graph embedding technique. However, they only capture the first-order proximity. To address this problem, we design DSTGeH, a deep self-taught graph embedding hashing framework which learns hash function without labels for image retrieval. DSTGeH introduces deep graph embedding means to capture more complex topological relationships (the second-order proximity) on the graph and maps these relationships into pseudo labels, which enables an end-to-end hash model and helps recognize the samples outside the graph. We present the ablation studies and compare DSTGeH with the state-of-the-art label-free hashing algorithms. Extensive experiments show DSTGeH can achieve the best performances and produce an overwhelming advantage on multi-object datasets.

The storage system in large scale data centers is typically built upon thousands or even millions of disks, where disk failures constantly happen. A disk failure could lead to serious data loss and thus system unavailability or even catastrophic consequences if the lost data cannot be recovered. While replication and erasure coding techniques have been widely deployed to guarantee storage availability and reliability, disk failure prediction is gaining popularity as it has the potential to prevent disk failures from occurring in the first place. Recent trends have turned toward applying machine learning approaches based on disk SMART attributes for disk failure predictions. However, traditional machine learning (ML) approaches require a large set of training data in order to deliver good predictive performance. In large-scale storage systems, new disks enter gradually to augment the storage capacity or to replace failed disks, leading storage systems to consist of small amounts of new disks from different vendors and/or different models from the same vendor as time goes on. We refer to this relatively small amount of disks as minority disks. Due to the lack of sufficient training data, traditional ML approaches fail to deliver satisfactory predictive performance in evolving storage systems which consist of heterogeneous minority disks. To address this challenge and improve the predictive performance for minority disks in large data centers, we propose a minority disk failure prediction model named TLDFP based on a transfer learning approach. Our evaluation results on two realistic datasets have demonstrated that TLDFP can deliver much more precise results, compared to four popular prediction models based on traditional ML algorithms and two state-of-the-art transfer learning methods.

Understanding and forecasting user visits is of great importance for a variety of tasks, e.g., online advertising, which is one of the most profitable business models for Internet services. Publishers sell advertising spaces in advance with user visit volume and attributes guarantees. There are usually tens of thousands of attribute combinations in an online advertising system. The key problem is how to accurately forecast the number of user visits for each attribute combination. Many traditional work characterizing temporal trends of every single time series are quite inefficient for large-scale time series. Recently, a number of models based on deep learning or matrix factorization have been proposed for high-dimensional time series forecasting. However, most of them neglect correlations among attribute combinations, or are tailored for specific applications, resulting in poor adaptability for different business scenarios.Besides, sophisticated deep learning models usually cause high time and space complexity. There is still a lack of an efficient highly scalable and adaptable solution for accurate high-dimensional time series forecasting. To address this issue, in this work, we conduct a thorough analysis on large-scale user visits data and propose a novel deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time series forecasting. We deployed the proposed framework in Tencent online guaranteed delivery advertising system, and extensively evaluated the effectiveness and efficiency of the framework in two different large-scale application scenarios. The results show that our framework outperforms existing methods in prediction accuracy. Meanwhile, it significantly reduces the parameter number and is resistant to incomplete data with up to 20% missing values.

Configuration tuning is vital to optimize the performance of database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts.

Recent advances in nanotechnology have provided the opportunity to significantly enhance the performance of hydrogen gas nanosensors. Our research focuses on the reliability of one particular nanosensor, a network of ultra small palladium nanowires, which detects hydrogen gas through a change in resistivity. The discrete random variable, representing the lifetime of the nanosensor, is defined as the number of exposures to, or cycles of, hydrogen gas that the nanosensor can withstand before it no longer functions. The nanosensor is modeled, and the reliability is analyzed under the assumption that the nanosensor is performing in an environment where the probability of a nanowire breaking changes after each cycle of hydrogen gas. Nanoscale components present unique difficulties when evaluating the reliability of any device. We attempt to resolve some of these issues by creating a flexible model that allows for the unknown characteristics of the nanosensor to be accounted for. Although this work is motivated by one particular nanosensor, our results can also be applied to assess the reliability of any nanodevice where our proposed model is a reasonable choice.