In the global Internet, understanding paths between autonomous systems (ASes) is valuable for improving the Internet routing system and optimizing various applications. However, due to the business and privacy concerns, only a small portion of paths are disclosed. Moreover, limited by the measurement resources, obtaining paths between any two ASes is impossible. Thus, path inference becomes necessary. Recent work proposes training individual model for each AS to infer paths, but it lacks personalization as it uses a shared approach and data for arbitrary ASes. Moreover, training models from scratch for all the ASes is time-consuming and resource-intensive. This paper introduces Personalized and Adaptive Generative Measurable Path Inference (PA-GMPI), a prefix-grained path inference process. PA-GMPI is capable of achieving superior performance and faster model training by fully leveraging the exclusive information of each AS. These improvements come from a personalized path generator, a 3-layer graph kernel based adaptive training warm-starter, and a real-world walks based AS representation learner. In evaluation, PA-GMPI significantly outperforms the state-of-the-art method, achieving a maximal accuracy improvement of 28.72% and ESR (exact same ratio) improvement of 49.95%. Furthermore, PA-GMPI achieves an average reduction of 20.21% in training resource consumption across over two thousand training sessions, using vantage ASes from five snapshots, which included 439 distinct ASes.

Large Language Models (LLMs) have demonstrated impressive performances across various NLP tasks with just a few prompts via in-context learning. Previous studies have emphasized the pivotal role of well-chosen examples in in-context learning, as opposed to randomly selected instances that exhibits unstable results.A successful example selection scheme depends on multiple factors, while in the context of LLMs-based machine translation, the common selection algorithms only consider the single factor, i.e., the similarity between the example source sentence and the input sentence.In this paper, we introduce a novel approach to use multiple translational factors for in-context example selection by using monotone submodular function maximization.The factors include surface/semantic similarity between examples and inputs on both source and target sides, as well as the diversity within examples.Importantly, our framework mathematically guarantees the coordination between these factors, which are different and challenging to reconcile.Additionally, our research uncovers a previously unexamined dimension: unlike other NLP tasks, the translation part of an example is also crucial, a facet disregarded in prior studies.Experiments conducted on BLOOMZ-7.1B and LLAMA2-13B, demonstrate that our approach significantly outperforms random selection and robust single-factor baselines across various machine translation tasks.

Video object detection is a challenging task due to deteriorated object appearances. In order to bolster per-frame feature representations, one way is to aggregate features from relevant frames. However, relying exclusively on RGB modal for feature aggregation may limit the detection performance for lacking of motion robustness. We propose a novel proposal distillation of multi-modal feature aggregation network (PDMAN). Specially, it initially aligns the feature domain and flow domain via a lightweight flow module (LFM) and then facilities frame-level feature aggregation. Subsequently, a global-based semantic embedding module (GSEM) is designed to incorporate global semantic features into instance features and introduce a global multi-label classification loss to guide encoding with high class-wise responsiveness. Finally, to alleviate the presence of insufficient and redundant information in multi-modal instance-level feature aggregation, a proposal distilled aggregation module (PDAM) is employed. By distilling the instance set, this approach realizes a fine-grained feature aggregation, ultimately boosting the detection performance. Experimental results demonstrate that the proposed PDMAN achieves a favorable result on the most representative large-scale ImageNet VID dataset.

Node and graph-level clustering hold considerable significance for a wide range of applications, including drug target identification and protein function prediction. Recently, contrastive learning has surpassed numerous unsupervised learning methods and become increasingly useful for various deep clustering procedures, achieving commendable results. However, two primary obstacles impede further deployment of graph contrastive clustering: (1) its inherent tendency to separate node representations, which contradicts the clustering objective of forming meaningful groups and impedes effective cluster creation, and (2) the occurrence of false negative samples, which similarly obstructs cluster formation. Hence, this paper proposes a novel graph clustering algorithm, which employs a dual contrastive learning approach, encompassing element and cluster contrasts, and a strategy for debiasing false negative samples. The proposed algorithm utilizes element-level contrastive learning on embeddings derived from the encoder, integrating detailed node or graph characteristics. Then, clustering and cluster-level contrastive learning are executed in the embedding space to refine the results. Furthermore, the algorithm effectively addresses the potential false negatives and imbalanced prediction challenges during the dual-contrast process by implementing an optimization mechanism based on reliable results, thereby enhancing the clustering performance. Rigorous experiments across three node and graph-level benchmarks validate our proposed algorithm's efficacy.

In this paper, an emotional intelligent terminal sliding mode controller is exploited. First, the dynamic model of a second-order nonlinear system considering unknown external disturbances is presented. The fast terminal sliding mode control (FTSMC) scheme is proposed in order to impose superior convergence feature. In addition, from the perspective of further improving the system performance and solving the problem of a prior knowledge dependence in FTSMC design, an emotional intelligent control framework with a self-construction mechanism is proposed to develop a model-free emotional intelligent terminal sliding mode controller (EITSMC). Moreover, an adaptive auxiliary item is developed to further strengthen the system robustness. Finally, the superior control abilities of the proposed strategy are evaluated by simulation and prototype experiments.

Video object detection is a challenging task due to deteriorated object appearances. To enhance per-frame features, one way is to aggregate features from several support frames. However, proposals generated by the region proposal network may not be precise and diverse due to the fixed anchors, limiting the detection performance. We propose a novel architecture called Diversity-Focused Network (DF-Net), which consists of three modules: 1) An affine transform module (ATM), which is proposed to model the deblurring process and fuse the feature maps of different receptive fields by a multi-level attention block; 2) A label assignment module (LAM), which is proposed to assign the labels to the proposals used in a fine-grained aggregation manner; 3) A regression-guided diffusion module (RGDM), which is proposed to obtain the features of diversity and higher quality. Experiments show that DF-Net achieves favorable results on the most representative large-scale ImageNet VID dataset. Remarkably, the DF-Net achieves 84.8% mAP with ResNet-101 without post-processing steps.

Link prediction has attracted considerable attention. Empiricism and the evolution mechanism based approach are the mainstream methods for link prediction. However, one drawback of such approaches is that they usually ignore the dynamic evolution mechanism of social networks, yet being dynamic is an essential characteristic of a social network that exists in every stage of the networks evolution. In this paper, we address the problem of temporal link prediction and investigate social networks from the time dimension with the purpose of dynamic evolution mechanism capturing. First, we separate a temporal network into a series of snapshots. Then, we propose a triad transition matrix prediction algorithm to learn the change of the distribution of triads among the different snapshots. The learned changes in the distribution of triads can capture the dynamic evolution of the network. With a proposed triad transition influence quantification algorithm, we propose a motifs based link prediction method for temporal link prediction. The proposed method can capture the dynamic evolution of temporal networks and is universal than existing methods. Extensive experiments on disparate real-world networks and model networks with controllable evolution demonstrate the effectiveness of the proposed method.

Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the structure and inherent properties of the network. Most existing network embedding methods didn't consider network noise. However, it is almost impossible to observe the actual structure of a real-world network without noise. The noise in the network will affect the performance of network embedding dramatically. In this paper, we aim to exploit node similarity to address the problem of social network embedding with noise and propose a node similarity preserving (NSP) embedding method. NSP exploits a comprehensive similarity index to quantify the authenticity of the observed network structure. Then we propose an algorithm to construct a correction matrix to reduce the influence of noise. Finally, an objective function for accurate network embedding is proposed and an efficient algorithm to solve the optimization problem is provided. Extensive experimental results on a variety of applications of real-world networks with noise show the superior performance of the proposed method over the state-of-the-art methods.

Wireless on-demand data broadcasting (ODDB) has become the preferred method of disseminating data to a large number of mobile users. The rapid boom in client requests has resulted in an urgent demand for improved ODDB system capacity (number of service users), especially in client-intensive and request-frequent environments. There are two bottlenecks in ODDB systems: the base station access capacity and the server concurrency capability. However, existing algorithms do not consider these bottlenecks, leading to deficient performance in ODDB system capacity and broadcast efficiency. In this paper, a three-layer ODDB system architecture and a request pre-processing method (RPPM) are proposed. The innovations of this study are twofold: (1) The three-layer ODDB system architecture enhances the base station access capacity by introducing a virtual node layer to share the high load of the base station. (2) Based on the proposed architecture, the RPPM handles request merger and priority evaluation to reduce server concurrency and improve broadcast efficiency. The results of experiments and analyses reveal that the proposed method can significantly enhance the ODDB system capacity and obtain outstanding broadcast efficiency.

Recently, social networks have witnessed a massive surge in popularity. A key issue in social network research is network evolution analysis, which assumes that all the autonomous nodes in a social network follow uniform evolution mechanisms. However, different nodes in a social network should have different evolution mechanisms to generate different edges. This is proposed as the underlying idea to ensure the nodes' evolution diversity in this paper. Our approach involves identifying the micro-level node evolution that generates different edges by introducing the existing link prediction methods from the perspectives of nodes. We also propose the edge generation coefficient to evaluate the extent to which an edge's generation can be explained by a link prediction method. To quantify the nodes' evolution diversity, we define the diverse evolution distance. Furthermore, a diverse node adaption algorithm is proposed to indirectly analyze the evolution of the entire network based on the nodes' evolution diversity. Extensive experiments on disparate real-world networks demonstrate that the introduction of the nodes' evolution diversity is important and beneficial for analyzing the network evolution. The diverse node adaption algorithm outperforms other state-of-the-art link prediction algorithms in terms of both accuracy and universality. The greater the nodes' evolution diversity, the more obvious its advantages.