5G networks and Subband Full Duplex (SBFD) technology aim to enhance uplink transmission efficiency in IoT environments. However, large-scale random access users in SBFD systems exacerbates cross-link interference (CLI), which could undermine its potential performance advantages. To address this challenge, we propose a priority-aware random access (PA-RA) strategy, which comprises two key components. First, access users are selected through a dynamic priority evaluation method that considers Quality of Service (QoS), real-time resources availability, and historical access attempts. Second, a centralized subband allocation method preferentially assigns resources to central regions of the uplink subband to minimize CLI, while adaptively utilizing edge resources under low-interference conditions. The simulation results demonstrate that PA-RA strategy could significantly reduce CLI compared to conventional methods, achieving high success rates of access and low latency. Furthermore, it provides a robust framework for interference management in SBFD networks with high-density IoT devices.

Protein clustering and classification are critical for understanding protein functions and interactions, particularly within structure-based predictions. Traditional sequence-based clustering often overlooks the pivotal role of tertiary structure in determining protein function. Structural clustering remains limited and challenging, with existing methods struggling to achieve high accuracy and manage complex data. This study focuses on the tertiary structures of Verticillium dahliae proteins, employing deep learning techniques for effective clustering and classification. Using AlphaFold2, we predicted protein structures and generated Cα atom distance matrices. We introduced a novel Unique Nuclear Sequence Element (UNSE) neural network to enhance feature extraction, constructing weighted distance matrices by integrating Cα distances with Pfam annotations. This method effectively captures complex structural relationships. Additionally, Basic Local Alignment Search Tool (BLAST) sequence alignments validated the sequence similarity within protein families, ensuring the biological relevance of clustering results. We applied clustering algorithms to both raw and weighted matrices, comparing their performance against traditional sequence-based and other structure-based methods, including DeepGO and DeepFRI. Evaluation metrics such as Silhouette Score, ${F}_{max}$, and AUPR demonstrated that our weighted matrix approach significantly outperforms conventional methods in accuracy and robustness. These findings confirm that integrating deep learning with weighted distance matrices effectively captures structural and functional protein characteristics, providing a robust tool for structural biology.

Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing view adaptation. Besides, noise or outliers might significantly degrade the overall clustering performance, which are not handled well by most existing methods. In this paper, we propose a novel unified framework for incomplete and complete MVC named self-learning symmetric multi-view probabilistic clustering (SLS-MPC). SLS-MPC proposes a novel symmetric multi-view probability estimation and equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution. Next, graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, SLS-MPC proposes a probabilistic clustering algorithm to adjust clustering assignments by maximizing the joint probability iteratively without category information. Extensive experiments on multiple benchmarks show that SLS-MPC outperforms previous state-of-the-art methods.

Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. Discriminative methods usually incorporate the hierarchical structure information into the encoding process, while generative methods decode the features according to it. However, the data distribution varies widely among different categories of samples, but current methods ignore the data imbalance, making the predictions biased and susceptible to error propagation. In this paper, we propose an IMplicitly Augmented Generativ E framework with distribution modification for hierarchical text classification (IMAGE). Specifically, we translate the distributions of original samples along various directions through implicit augmentation to get more diverse data. Furthermore, given the scarcity of the samples of tail classes, we adjust their distributions by transferring knowledge from other classes in label space. In this way, the generative framework learns a better beginning of the feature sequence without a prediction bias and avoids being misled by its wrong predictions for head classes. Experimental results show that IMAGE obtains competitive results compared with state-of-the-art methods and prove its superiority on unbalanced data.

Active clustering aims to boost the clustering performance by integrating human-annotated pairwise constraints through strategic querying. Conventional approaches with semi-supervised clustering schemes encounter high query costs when applied to large datasets with numerous classes. To address these limitations, we propose a novel Adaptive Active Aggregation and Splitting (A3S) framework, falling within the cluster-adjustment scheme in active clustering. A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In particular, our cluster adjustment is inspired by the quantitative analysis of Normalized mutual information gain under the information theory framework and can provably improve the clustering quality. The proposed A3S framework significantly elevates the performance and scalability of active clustering. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries compared with existing methods.

Emotion-Cause Pair Extraction (ECPE) aims to identify the document's emotion clauses and corresponding cause clauses.Like other relation extraction tasks, ECPE is closely associated with the relationship between sentences.Recent methods based on Graph Convolutional Networks focus on how to model the multiplex relations between clauses by constructing different edges.However, the data of emotions, causes, and pairs are extremely unbalanced, but current methods get their representation using the same graph structure.In this paper, we propose a Joint Constrained Learning framework with Boundary-adjusting for Emotion-Cause Pair Extraction (JCB).Specifically, through constrained learning, we summarize the prior rules existing in the data and force the model to take them into consideration in optimization, which helps the model learn a better representation from unbalanced data.Furthermore, we adjust the decision boundary of classifiers according to the relations between subtasks, which have always been ignored.No longer working independently as in the previous framework, the classifiers corresponding to three subtasks cooperate under the relation constraints.Experimental results show that JCB obtains competitive results compared with state-of-theart methods and prove its robustness on unbalanced data.

Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance. In this paper, we propose a novel unified framework for incomplete and complete MVC named multi-view probabilistic clustering (MPC). MPC equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, MPC also equivalently transforms probabilistic clustering's objective to avoid complete pairwise computation and adjusts clustering assignments by maximizing joint probability iteratively. Extensive experiments on multiple benchmarks for incomplete and complete MVC show that MPC significantly outperforms previous state-of-the-art methods in both effectiveness and efficiency.

Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention.Previous methods sequentially encode features with a specified order.They first encode the emotion and cause features for clause extraction and then combine them for pair extraction.This lead to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former.To address this issue, we propose a novel Pair-Based Joint Encoding (PBJE) network, which generates pairs and clauses features simultaneously in a joint feature encoding manner to model the causal relationship in clauses.PBJE can balance the information flow among emotion clauses, cause clauses and pairs.From a multi-relational perspective, we construct a heterogeneous undirected graph and apply the Relational Graph Convolutional Network (RGCN) to capture the various relationship between clauses and the relationship between pairs and clauses.Experimental results show that PBJE achieves state-of-the-art performance on the Chinese benchmark corpus.

Object detection from still images has been among the most active and challenging area in computer vision recently. In contrast, fully supervised object detection from video has rarely been investigated. In this paper, we propose an algorithm to improve the performance of object detection from video. Our proposed method is based on an empirical property that the trajectory of an object is important for detection in videos. We use object trajectory to filter outliers, determine probable location of object and correct detection errors. We compare our method with baseline method which regard video frames as still images directly. Experiments show that our method outperform the compared baseline in terms of average precision while inducing moderate computation overhead.