Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PLM-based methods ignore the information of trigger/argument fields, which is crucial for understanding event schemas. To this end, we propose a Probabilistic reCoupling model enhanced Event extraction framework (ProCE). Specifically, we first model the syntactic-related event fields as probabilistic biases, to clarify the event fields from ambiguous entanglement. Furthermore, considering multiple occurrences of the same triggers/arguments in EE, we explore probabilistic interaction strategies among multiple fields of the same triggers/arguments, to recouple the corresponding clarified distributions and capture more latent information fields. Experiments on EE datasets demonstrate the effectiveness and generalization of our proposed approach.

Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a Graph enhanced Entity Retrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models.

Dynamic textures (DTs) that represent moving scenes such as flames, smoke, and waves, exhibit fixed dynamics within a period of time and have been successfully modeled using linear dynamic systems (LDS). In this paper, we show that the widely used LDS model can be approximated using a principal component regression (PCR) model with the main advantage of simplicity. Furthermore, to capture the nonlinearity of training frames, we extend traditional PCR to its kernelized version and introduce kernel principal component regression (KPCR) to model and synthesize DTs. To ensure algorithm stability, we remove the standard state model and directly apply the quantized kernel least mean squares algorithm from signal processing domain to approximate the performance achieved with KPCR. We term this improvement kernel adaptive dynamic texture synthesis (KADTS), which also has the benefits of computational and memory efficiency. These advantages make KADTS ideally suited for real-world applications, since the majority of electronic devices, including cell phones and laptops, suffer from limited memory and real-time constraints. We demonstrate, via both theoretical and experimental analyses, the connections between DT synthesis using KPCR and KADTS with a regularization network theory. We also show the superiority of our proposed algorithms for DT synthesis compared with other dynamic system-based benchmarks. MATLAB code is available from our project homepage http://bmal.hust.edu.cn/project/dts.html.

This paper addresses the problem of synthesizing continuous and infinitely varying stream of texture videos by doing operations on finite texture videos. Given an input texture video, such as flame, water, smoke, etc, we can synthesize a longer texture video holding the same texture appearance. Dynamic textures have been modeled as linear dynamic systems (LDS) by unfolding the video frames into column vectors and modeling their dynamic trajectory as time evolves. After the vectors are projected onto a lower dimensional space by Singular Value Decomposition (SVD), dynamic texture synthesis is achieved by driving the system with random noise. However, because of its over-simplified appearance model and under-constrained dynamic model. It is usually hard to synthesize long and visual pleasing texture video sequences. In this paper, we propose a new dynamic texture synthesis framework via creatively fitting the basic LDS with a newly developed patch reconstruction technique to efficiently enhance high quality texture details while maintaining the temporal coherence of the reconstructed texture patches. The patch reconstruction technique is inspired by locally linear embedding (LLE) and based on the assumption that small patches in the low-and high-quality images form manifolds with similar local geometry. The newly synthesized patches are finally stitched together by graph cuts to make up the output texture videos. Experiments on standard dynamic texture databases demonstrate that our method exhibits superior performance on synthesizing dynamic textures.

Following the widely use of search engines, the impact Web robots have on the Web sites should not be ignored. After analyzing the navigational patterns of Web robots from Web logs, two new algorithms are proposed. One is based on classification and statistics of requested URL resources, which classifies the URL resources into eight types and counts the number of session of the clients and number of visiting records with same type. And another is based on Web page member list, which constructs one member list for every Web page and one show table for every visitor. The experiment shows that the two new algorithms can detect the unknown robots and unfriendly robots who do not obey the standard for robot exclusion.