TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition

Second-order feature statistics are central to texture recognition, yet current methods face a fundamental tension: bilinear pooling and Gram matrices capture global channel correlations but collapse spatial structure, while self-attention models spatial context through weighted aggregation rather than explicit pairwise feature interactions. We introduce TwistNet-2D, a lightweight module that computes \emph{local} pairwise channel products under directional spatial displacement, jointly encoding where features co-occur and how they interact. The core component, Spiral-Twisted Channel Interaction (STCI), shifts one feature map along a prescribed direction before element-wise channel multiplication, thereby capturing the cross-position co-occurrence patterns characteristic of structured and periodic textures. Aggregating four directional heads with learned channel reweighting and injecting the result through a sigmoid-gated residual path, \TwistNet incurs only 3.5% additional parameters and 2% additional FLOPs over ResNet-18, yet consistently surpasses both parameter-matched and substantially larger baselines -- including ConvNeXt, Swin Transformer, and hybrid CNN--Transformer architectures -- across four texture and fine-grained recognition benchmarks.

OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization

Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabulary of search operators and learn this operator program separately for each problem instance. We instantiate this idea in Operator-Programmed Algorithms (OPAL), a landscape-aware framework for continuous black-box optimization that uses a small design budget with a standard differential evolution baseline to probe the landscape, builds a $k$-nearest neighbor graph over sampled points, and encodes this trajectory with a graph neural network. A meta-learner then maps the resulting representation to a phase-wise schedule of exploration, restart, and local search operators. On the CEC~2017 test suite, a single meta-trained OPAL policy is statistically competitive with state-of-the-art adaptive differential evolution variants and achieves significant improvements over simpler baselines under nonparametric tests. Ablation studies on CEC~2017 justify the choices for the design phase, the trajectory graph, and the operator-program representation, while the meta-components add only modest wall-clock overhead. Overall, the results indicate that operator-programmed, landscape-aware per-instance design is a practical way forward beyond ad hoc metaphor-based algorithms in black-box optimization.

Time-Fair Benchmarking for Metaheuristics: A Restart-Fair Protocol for Fixed-Time Comparisons

Numerous purportedly improved metaheuristics claim superior performance based on equivalent function evaluations (FEs), yet often conceal additional computational burdens in more intensive iterations, preprocessing stages, or hyperparameter tuning. This paper posits that wall-clock time, rather than solely FEs, should serve as the principal budgetary constraint for equitable comparisons. We formalize a fixed-time, restart-fair benchmarking protocol wherein each algorithm is allotted an identical wall-clock time budget per problem instance, permitting unrestricted utilization of restarts, early termination criteria, and internal adaptive mechanisms. We advocate for the adoption of anytime performance curves, expected running time (ERT) metrics, and performance profiles that employ time as the cost measure, all aimed at predefined targets. Furthermore, we introduce a concise, reproducible checklist to standardize reporting practices and mitigate undisclosed computational overheads. This approach fosters more credible and practically relevant evaluations of metaheuristic algorithms.

Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification

Twisted Convolutional Networks (TCNs) are introduced as a novel neural network architecture designed to effectively process one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike traditional Convolutional Neural Networks (CNNs), which excel at handling structured two-dimensional data like images, TCNs reduce dependency on feature order by combining input features in innovative ways to create new representations. By explicitly enhancing feature interactions and employing diverse feature combinations, TCNs generate richer and more informative representations, making them especially effective for classification tasks on datasets with arbitrary feature arrangements. This paper details the TCN architecture and its feature combination strategy, providing a comprehensive comparison with traditional CNNs, DeepSets, Transformers, and Graph Neural Networks (GNNs). Extensive experiments on benchmark datasets demonstrate that TCNs achieve superior performance, particularly in classification scenarios involving one-dimensional data.

Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections

In traditional boosting algorithms, the focus on misclassified training samples emphasizes their importance based on difficulty during the learning process. While using a standard Support Vector Machine (SVM) as a weak learner in an AdaBoost framework can enhance model performance by concentrating on error samples, this approach introduces significant challenges. Specifically, SVMs, characterized by their stability and robustness, may require destabilization to fit the boosting paradigm, which in turn can constrain performance due to reliance on the weighted results from preceding iterations. To address these challenges, we propose the Support Vector Boosting Machine (SVBM), which integrates a novel subsampling process with SVM algorithms and residual connection techniques. This method updates sample weights by considering both the current model's predictions and the outputs from prior rounds, allowing for effective sparsity control. The SVBM framework enhances the ability to form complex decision boundaries, thereby improving classification performance. The MATLAB source code for SVBM can be accessed at https://github.com/junbolian/SVBM.