Infrared and visible image fusion (IVIF) technology has become a frontier of great interest due to the ability to integrate information from multiple sources. However, the progressive slowdown of weight updates in deep networks (i.e., “network laziness” phenomenon), makes existing methods far from realizing the full characterization potential. To this end, we propose a lightweight fusion method for IVIF, Anti-Inert Dynamic Fusion (AIDFusion), to fully utilize the potential of the network at all levels. Specifically, by progressively regulating the collaborative Learning process of multi-level prediction in the network, Dynamic Inertia Inhibition Learning Strategy (DIILS) is proposed to adaptively and efficiently inhibit inertia accumulation. Subsequently, to deeply explore the representation potential while breaking through the performance threshold, lightweight Multi-dimensional modulation fusion module (MMFM) is specifically proposed to capture comprehensive multi-view and multi-scale features efficiently. Finally, considering the semantic bias between the prediction maps of DIILS and the fusion feature of MMFM, Fourier Analysis Convolution (FAConv) is designed in feature recovery as a bridge between prediction and fusion to accomplish the implicit periodic modeling. Based on the above study, extensive experiments on three public IVIF datasets demonstrate the dual advantages of AIDFusion in terms of fusion performance and computational overhead compared to state-of-the-art baseline methods.

In semi‑supervised multi‑view classification (SMVC), scarce labels and noisy unlabeled data impair feature aggregation and compromise prediction reliability, while existing methods lack principled guidance and interpretability. To overcome these limitations, we propose a novel unified SMVC framework, Neural Collapse Priors Driven Trust Semi-Supervised Multi-View Classification (NCPD-TSMVC), building upon neural collapse–derived prototype priors and evidential opinion fusion. Concretely, we rigorously prove under neural collapse theory that normalized classifier weights from the labeled‑data pre‑training stage coincide with class centroids in feature space, conferring maximal inter‑class separation and optimal within‑class compactness. These prototype priors permeate the entire learning pipeline, calibrating the representation learning of unlabeled samples to obtain highly discriminative embeddings. Simultaneously, our evidential learning module quantifies epistemic uncertainty and fuses view‑level opinions at the evidence level, yielding robust and transparent decision making. Extensive evaluations across diverse benchmarks demonstrate that NCPD‑TSMVC surpasses state‑of‑the‑art SMVC approaches in performance, robustness and interpretability.

The Audio-Visual Question Answering (AVQA) task involves extracting question-related audio-visual clues from both temporal and spatial perspectives to answer questions accurately. Despite the promising performance of existing multi-modal AVQA models, thanks to large-scale pre-trained models, challenges remain in the field. Firstly, aligning audio-visual information across temporal and spatial dimensions is difficult. Secondly, the fusion of audio-visual information is often weighted inadequately, limiting model performance. To address the above issues, we design the Audio-Visual Adaptive Fusion Network (AVAF-Net), which uses contrastive learning to align audio-visual information temporally and spatially and adaptively adjusts fusion weights based on the question. Specifically, we initially align visual and audio information temporally through a temporal-alignment contrastive loss. This is followed by an audio-visual clue-mining module that highlights question-related cues, aligning them with the vocal region spatially using spatial alignment contrastive loss. Additionally, a question-oriented adaptive fusion module assigns different weights to audio and visual modalities based on the question content and then fuses them. The fused audio-visual cues are finally used to predict the answer. Extensive experiments on the MUSIC-AVQA dataset show that AVAF-Net surpasses all baseline models, with a maximum improvement of 15.90% in average accuracy and an average improvement of 9.80%.

The decision tree algorithm is an effective machine learning technique, but it cannot uncover causal relationships within data. To overcome this limitation, the causal decision tree was proposed, which combines causal discovery with decision tree principles. This method is particularly effective at identifying causal relationships in complex datasets. Currently, causal decision tree algorithms are being applied in various fields, including biology and politics. However, existing causal decision tree algorithms do not account the fairness. To fill this gap, this paper proposes the Fair Causal Decision Tree (FCDT), constructed through a fair post-pruning process. The FCDT ensures the algorithm's fairness while maintaining the causality inherent in the causal decision tree. To preserve the algorithm's causality, this paper introduces the Causal Structure Change Constraint (CSCC), which guarantees that the causal decision tree retains its strong causal properties when handling unfair nodes. This innovative constraint ensures that any structural modifications during the fairness adjust-ment process almost not decrease the algorithm's causality. The experiments were conducted on multiple datasets, and the results demonstrate that the proposed algorithm enhances fairness by approximately 10% compared to traditional causal decision tree algorithms, with only about a 1 % decrease in recall rate.

Current methods for Music Emotion Recognition (MER) face challenges in effectively extracting features sensitive to emotions, especially those rich in temporal detail. Moreover, the narrow scope of music-related modalities impedes data integration from multiple sources, while including multiple modalities often leads to redundant information, which can degrade performance. To address these issues, we propose a lightweight framework for music emotion recognition that improves the extraction of features that are both sensitive to emotions and rich in temporal information and that integrates data from both audio and MIDI modalities while minimizing redundancy. Our approach involves developing two innovative unimodal encoders to learn embeddings from audio and MIDI-like features. Additionally, we introduce a Bi-modal Fusion Attention Model (BFAM) that integrates features from low-level to high-level semantic information across different modalities. Experimental evaluations on the EMOPIA and VGMIDI datasets show that our unimodal networks achieve accuracies that are 6.1% and 4.4% higher than baseline algorithms for MIDI and audio on the EMOPIA dataset, respectively. Furthermore, our BFAM achieves a 15.2% improvement in accuracy over the baseline, reaching 82.2%, which underscores its effectiveness for bi-modal MER applications.

Multimodal Summarization aims to use multimodal data to generate accurate and concise summaries for long sentences. While previous work has achieved promising success, they have overlooked the mismatching among multimodal semantics and lacked subject information guidance for adaptive referential images. Motivated by this observation, we propose ASSM, anAdaptiveSubject-focused modeling for multimodal summarization viaSemanticMatching. The novelty of ASSM lies in two aspects. First, we propose a multimodal semantic matching module that projects multimodal inputs into a shared joint embedding semantic space to determine whether the semantics between multimodalities are mismatching. Second, we propose an adaptive subject-focused guide module, which adaptively references images to learn subject tokens based on the multimodal semantic matching results. With these subject tokens, we are able to focus on the subject information, providing precise guidance for summary generation. We conduct extensive experiments on two standard benchmarks and compare ASSM with 17 existing models. The experimental results regarding ROUGE, BERTScore, and MoverScore show that the proposed ASSM model outperforms all competitors, achieving state-of-the-art performance and suggesting the effectiveness of our proposal. In addition, we provide a case study to further demonstrate the usability of ASSM.

In-memory learned index has been an efficient approach supporting in-memory fast data access. However, existing learned indexes are inefficient in supporting variable-length keys. To address this issue, we propose a new in-memory learned index called LIVAK that adopts a hybrid structure involving trie, learned index, and B+-tree. Each node indexes an 8-byte slice of keys, and we use learned indexes for large nodes but B+-trees for small nodes. Also, LIVAK presents a character re-encoding mechanism to avoid performance degradation. We compare LIVAK with B+-tree, Masstree, and SIndex on various datasets and workloads, and the results suggest the efficiency of LIVAK.

Learned indexes incorporate machine learning models to predict the locations of keys in the dataset. However, achieving accurate prediction is difficult due to the learning models’ inability to fully capture data distribution. Existing learned indexes used data partition or pre-set error bounds to solve this problem. However, data partition requires constructing a higher index structure, and pre-set error bounds have to train many learning models. In the paper, we propose a new Hierarchical Learned Index with High-Precision query on end nodes (HLIHP), which aims to achieve higher query precision with lower training cost. First, we propose a Precision Correction Model to correct the prediction results, which associates the predicted results with the real positions of the keys. Then, a lightweight learned model is used to construct the superstructure of the index, which can find the end nodes quickly with low training cost and effectively support the query operation. We conduct experiments on four datasets, including covid, genome, osm, and planet, to evaluate the performance of our proposal. The results show that compared to the five hierarchical learned indexes, RMI, PGM-index, XIndex, FINEdex, and ALEX, HLIHP shows on average 2.42×, 1.78×, 5.68×, 6.11×, and 1.48× higher throughput in lookup performance.

The fairness and bias of spectral clustering algorithms have attracted considerable research interest in recent years. Currently fair spectral clustering algorithms are based on the notions of group fairness and individual fairness, which effectively reduce decision bias for similar individuals and sensitive groups. Existing fair spectral clustering algorithms achieve a certain degree of resource redistribution during the clustering process for a particular individual or part of a group, but there is still a situation where the final decision is unfair to the oversized or undersized result clusters. To this end, we present the first principled study of Scale Fairness on Spectral Clustering and propose the SFSC algorithm, which aims to effectively reduce the possibility of the results being oversized or undersized clusters by introducing entropy computation into the spectral clustering process. We measure the scale fairness of clusters by two statistical metrics, and demonstrate on eight classical and real-world datasets that SFSC has better fairness performance compared to spectral clustering while having comparable clustering effect. To the best of our knowledge, this paper is the first study to propose scale fairness for spectral clustering.

Hybrid memory composed of DRAM and persistent memory (PM) offers a promising way to realize large-capacity main memory supporting in-memory data storage and computing. However, traditional buffer management schemes focus on improving the hit ratio but lack awareness of the limitations of PM, e.g., slower write time and lower write endurance than DRAM. Therefore, developing new buffer management policies that can reduce costly write-backs of PM blocks while maintaining high performance for the hybrid buffer, is of paramount importance. Existing approaches mainly use a page-grained buffering policy, which will cause unnecessary data migrations between DRAM and PM, leading to a high number of disk I/Os and PM writes. Aiming to reduce I/O costs and PM writes, we propose a new buffer manager named HiBuffer for DRAM/PM-based hybrid memory systems. HiBuffer presents several novel ideas. First, it adopts multigrained data layouts to manage the hybrid buffer cooperatively. In addition to the page granularity, we introduce Lines for the DRAM buffer and Sectors to the PM buffer, forming a buffer with three granularities, including Line, Sector, and Page. We prove that the multigrained cooperative buffer management can deliver higher performance than existing page-grained schemes. Second, we propose a sector-grained method to migrate data from DRAM to PM, which can avoid unnecessary data movements and reduce PM writes. Third, we use an out-of-place updating mechanism to absorb updates in DRAM, which can further reduce the writes to PM. We compare HiBuffer with three existing schemes, including LRU, CLOCK-DWF, and MiniPage, on five synthetic workloads and the YCSB benchmark using real Intel Optane DC PM. The results in terms of various metrics, including running time, PM writes, hit ratio, and disk I/Os, suggest the efficiency of HiBuffer. In particular, HiBuffer reduces the running time by up to 37.8% and the writes to PM by up to 83% compared to the competitors when evaluated on the YCSB benchmark.

Fuzzy C-Means (FCM) clustering is a classic clustering algorithm, which is widely used in the real world. Despite the distinct advantages of FCM algorithm, whether the usage of fairness constraint in the FCM could improve clustering fairness remains fully elusive. By introducing a novel fair loss term into the objective function, a Fair Fuzzy C-Means (FFCM) algorithm was proposed in this current study. We proved that the membership value was constrained by distance and fairness in the meantime during the optimization process in the proposed objective function. By studying the Fuzzy C-Means Clustering with fairness constraint problem and proposing a fair fuzzy C-means method, this study provided mechanism understanding in achieving the fairness constraint in Fuzzy C-Means clustering and bridged up the gap of fair fuzzy clustering.

In this paper, we demonstrate Post2Story, which aims to detect events and generate storylines on microblog posts. Post2Story has several new features: (1) It proposes to employ social influence to extract events from microblogs. (2) It presents a new Event Graph Convolutional Network (E-GCN) model to learn the latent relationships among events, which can help predict the story branch of an event and link events. (3) It offers a user-friendly interface to extract and visualize the development of events. After an introduction to the system architecture and key technologies of Post2Story, we demonstrate the functionalities of Post2Story on a real dataset.

This paper demonstrates a prototype system called Post2Event that aims to extract key events from microblogs.While many events are hidden in microblogs, people may only care about those critical events, which are named key events in Post2Event.Specially, we propose to model the topic-related significance of an event and integrate the influence with the temporal characteristics of the event to measure the event's importance.We briefly present the architecture and technical details of Post2Event.Then, we report the comparative results of Post2Event on a real dataset.Finally, we demonstrate the running process of the system.

Spectral clustering is a popular clustering method because it gives a natural way to reduce the dimensionality of data using eigenvectors. It is well known that the performance of spectral clustering could be improved via regularization. Nevertheless, it is hard to cope with the different cases by only one constant regularization parameter. To solve such a problem, a novel regularized spectral clustering method is proposed. Specifically, two modules are integrated in the proposed method. First, under matrix perturbation analysis, we prove that the entropy can be used as a rank score function to reveal the informative eigenvector, and the eigenvector corresponding to the minimal entropy will be the regularization to regularize the data matrix instead of a constant regularization parameter. Second, in order to ensure the perturbation on eigenspace is within the effective range, a perturbation boundary on eigenvectors is given. Numerical results showed that our proposal has superior performance than spectral clustering and k-means algorithm.

There are a great number of complex relations among different characters in an opera. Retrieving such relations is crucial for performers and audience to accurately understand the features and behaviour of roles. Aiming to automatically extract relations among characters in an opera, in this paper we propose an effective method that can extract character relations from opera scripts. Firstly, we construct a uniform reasoning framework for opera scripts. Based on this model, we propose a deep syntax-parsing method to detect character relations from opera scripts. After that, we propose a new deep learning approach called SL-Bi-LSTM-CRF to extract the objects involved in character relations. The proposed SL-Bi-LSTM-CRF algorithm is a sentence-level relation extraction algorithm based on the Bi-directional LSTM with a CRF layer. With this mechanism, we are able to get a detailed description for character relations. We conduct experiments on a real dataset of opera scripts. The experimental results in terms of precision, recall, and F-score suggest the effectiveness of our proposal.

Most Web pages contain temporal information, which can be utilized by search engines to improve searching performance for users. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper we present and implement a prototype system for time-sensitive queries, which is called TASE (Time-Aware Search Engine). TASE extracts both the explicit and implicit temporal expressions for each Web page, and calculates the relevant score between the Web page and each temporal expression, and then re-rank search results based on the temporal-textual relevance between Web pages and the queries. It is demonstrated that TASE can improve the effectiveness of temporal-textual Web queries.