Accurate traffic flow prediction is fundamental to intelligent transportation systems, yet it remains challenging due to the complex spatio-temporal dynamics of road networks. Most existing approaches model traffic conditions as discrete snapshots, which fail to capture the underlying continuous evolution of traffic states and lead to significant information loss. Moreover, many methods insufficiently model spatial heterogeneity, inadequately distinguishing between local geographic proximity and global semantic correlations. To address these limitations, we propose Spatio-Temporal Forecasting with Adaptive Continuous learning and Heterogeneity awareness (STFACH), a novel framework for traffic forecasting. First, we introduce an Adaptive Continuous Learning module that bridges discrete observations and continuous dynamics, while capturing intrinsic volatility often filtered out by deterministic interpolation. Driven by a learnable control path derived from intrinsic traffic volatility, this module leverages attentive neural differential equations to reconstruct continuous latent trajectories and explicitly model fine-grained temporal evolution. Second, we design a Heterogeneity-Aware module to resolve spatial ambiguity by disentangling spatial dependencies into dual branches, constructing adaptive adjacency matrices that jointly capture local geometric relations and global semantic affinities. Extensive experiments on ten real-world benchmark datasets spanning diverse traffic scenarios demonstrate that STFACH significantly outperforms state-of-the-art baselines, validating its effectiveness and robustness in modeling both continuous dynamics and structural heterogeneity. The code is publicly available at https://github.com/MaxRubby/STFACH_2026.

The rapid expansion of protein sequence databases has created challenges for efficient storage, transmission, and analysis. Unlike genomic sequences with only four nucleotide bases, proteins are composed of twenty amino acids, making compression more complex. Existing specialized protein compressors, such as AC, AC2 and CPM-FCM, have achieved promising performance but still face limitations, including high computational cost, low adaptability and limited biological interpretability. This paper introduces GMP (Genetic algorithm-based MDL Protein compressor), a novel protein compression framework that leverages the Minimum Description Length (MDL) principle with a genetic algorithm to discover optimal patterns of amino acid subsequences (kAA-mers). Experimental results demonstrate that GMP attains compression performance comparable to state-of-the-art methods while additionally supporting tasks such as classification and clustering-capabilities absent from traditional protein compressors. This makes GMP not only an efficient compression framework but also a biologically interpretable tool for protein sequence analysis. GMP is available at github.com/MuhammadzohaibNawaz/GMP

Image-text retrieval is one of the major tasks of cross-modal retrieval. Several approaches for this task map images and texts into a common space to create correspondences between the two modalities. However, due to the content (semantics) richness of an image, redundant secondary information in an image may cause false matches. To address this issue, this paper presents a semantic optimization approach, implemented as a Visual Semantic Loss (VSL), to assist the model in focusing on an image’s main content. This approach is inspired by how people typically annotate the content of an image by describing its main content. Thus, we leverage the annotated texts corresponding to an image to assist the model in capturing the main content of the image, reducing the negative impact of secondary content. Extensive experiments on two benchmark datasets (MSCOCO and Flickr30K) demonstrate the superior performance of our method. The code is available at: https://github.com/ZhangXu0963/VSL.

Abstract Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP‐Growth have been proposed. FP‐Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP‐Growth. However, most of them still suffer from performance issues on large datasets. In this paper, we design an auxiliary structure, Array Prefix‐Tree (AP‐Tree), and propose a new algorithm, Array Prefix‐Tree Growth (APT‐Growth), which is further parallelized as a Spark workflow, referred to as PAPT‐Growth. Based on a density threshold, we incorporate an adaptive algorithm selection process into PAPT‐Growth to ensure its running time performance. We conduct extensive experiments on different thresholds and multiple datasets, and experimental results show the performance superiority of PAPT‐Growth in comparison with several state‐of‐the‐art methods such as PFP, YAFIM, and DFPS. The analysis on density reveals a changing point, which justifies the necessity and validity of adaptive algorithm selection.

Significant place mining in spatiotemporal trajectory data is a key task for mobile pattern mining, useful for supporting location-aware services. State-of-the-art trajectory clustering algorithms utilize a density-based distance measure. However, some major problems with this approach are that (1) results are often inaccurate, especially on data of varying density, (2) the user must fine-tune many thresholds that are unintuitive to set, and (3) boundary points between clusters are often assigned to the wrong locations. Performance is also a major issue as many state-of-the-art algorithms have a very high time complexity. Motivated by these issues, this paper proposes an approach inspired by the data field theory and community detection. It is a graph-based significant place mining algorithm, called GB-SPM, for capturing and characterizing the essence of similarity between nodes. GB-SPM first applies a novel low index neighborhood velocity point filtration method to extract characteristic points. Then, a characteristic point index neighborhood is used to map them to graph nodes. In this way, the original problem is transformed into a community detection problem in complex community networks. Finally, a new edge weight metric is proposed to capture and characterize the nature of similarity between nodes. To evaluate clustering quality, we used the Silhouette (SI) for unannotated data to value inter-cluster separation and intra-cluster homogeneity. To evaluate mining effectiveness, we used Matthew’s correlation coefficient (MCC) for annotated data. Numerous experiments were carried out on real world datasets, and the accuracy and performance of the designed algorithm was compared with the state-of-the-art algorithms. Results show that GB-SPM improves on average SI by 13.9%, MCC by 20.7%, and runtime by 5.15 times.

With the rapid increase in GPS data collection through pervasive use of mobile devices, it has become an important problem to discover significant places of moving objects from complex spatial and temporal trajectories. This problem is challenging mainly because such trajectory data suffer from several issues including incompleteness, low quality, high redundancy, and oftentimes trajectory points do not follow Gaussian distribution . We propose a clustering-based method with temporal and spatial semantics, referred to as Stops and Moves of Trajectories using Attribute Selection (SMoTAS), whose technical advantages are multifold. Firstly, it improves data availability by using a self-adaptive algorithm to correct the deviation in traditional speed-based methods. Secondly, it improves place mining accuracy by filtering multi-label clustering results when there is a lack of detailed geographic data. Thirdly, it employs feature selection to exploit the core attributes of clustering and simplify the clustering results with Grubbs criterion. Experimental results on real-life datasets show that SMoTAS not only achieves substantial improvement of accuracy over existing methods in discovering significant places, but also exhibits superior adaptability to different trajectories and application scenarios.

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

In the data mining of road networks, trajectory clustering of moving objects is of particular interest for its practical importance in many applications. Most of the existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, and incompetency to handle high dimensional trajectory data. This paper investigates the complex network theory and explores its application to trajectory clustering in road networks to address these issues. Specifically, we model a road network as a dual graph, which facilitates an effective transformation of the clustering problem from sub-trajectories in the road network to nodes in the complex network. Based on this model, we design a label-based trajectory clustering algorithm, referred to as LBTC, to capture and characterize the essence of similarity between nodes. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Davies-Bouldin Index (DB), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) to maximize inter-cluster separation and intra-cluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated by extensive simulations on both synthetic and real-world dataset in comparison with existing algorithms.