Large Language Models (LLMs) with extended context lengths face significant computational challenges during the pre-filling phase, primarily due to the quadratic complexity of self-attention. Existing methods typically employ dynamic pattern matching and block-sparse low-level implementations. However, their reliance on local information for pattern identification fails to capture global contexts, and the coarse granularity of blocks leads to persistent internal sparsity, resulting in suboptimal accuracy and efficiency. To address these limitations, we propose AnchorAttention, a difference-aware, dynamic sparse attention mechanism that efficiently identifies critical attention regions at a finer stripe granularity while adapting to global contextual information, achieving superior speed and accuracy. AnchorAttention comprises three key components: (1) Pattern-based Anchor Computation, leveraging the commonalities present across all inputs to rapidly compute a set of near-maximum scores as anchor; (2) Difference-aware Stripe Sparsity Identification, performing difference-aware comparisons with anchor to quickly obtain discrete coordinates of significant regions in a stripe-like sparsity pattern; (3) Fine-grained Sparse Computation, replacing the traditional contiguous loading strategy with a discrete key-value loading approach to maximize sparsity rates while preserving hardware computational potential. Additionally, we integrate the identification strategy into a single operator to maximize parallelization potential. With its finer-grained sparsity strategy, AnchorAttention achieves higher sparsity rates at the same recall level, significantly reducing computation time. Compared to previous state-of-the-art methods, at a text length of 128k, it achieves a speedup of 1.44\times while maintaining higher recall rates.

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs, basic fusion methods, and large model parameters, making it difficult to meet the performance requirements for practical deployment. To address these issues, this paper proposes the BiT-Align image-depth-text affordance mapping framework. The framework includes a Bypass Prompt Module (BPM) and a Text Feature Guidance (TFG) attention selection mechanism. BPM integrates the auxiliary modality depth image directly as a prompt to the primary modality RGB image, embedding it into the primary modality encoder without introducing additional encoders. This reduces the model’s parameter count and effectively improves functional region localization accuracy. The TFG mechanism guides the selection and enhancement of attention heads in the image encoder using textual features, improving the understanding of affordance characteristics. Experimental results demonstrate that the proposed method achieves significant performance improvements on public AGD20K and HICO-IIF datasets. On the AGD20K dataset, compared with the current state-of-the-art method, we achieve a 6.0% improvement in the KLD metric, while reducing model parameters by 88.8%, demonstrating practical application values. The source code will be made publicly available at https://github.com/DAWDSE/BiT-Align.

Text-to-Visualization (Text2Vis) generates data visualizations directly from natural language queries, democratizing access to data insights. Early Text2Vis efforts, primarily relying on rule-based systems and machine learning models, struggled to handle semantically intricate queries. The advent of large language models (LLMs) allows for better generalization in generating visualization code. However, LLM-based approaches have mainly focused on textual or code-level optimizations, neglecting the potential benefits of assessing and improving visualized charts. Hence, we propose Visualization Refinement (VisRef), a novel framework based on vision-language models (VLMs) to enhance Text2Vis outputs. (1) Knowledge Extraction -- VisRef extracts visualization assessment knowledge through a hierarchical contrastive prompt and multi-granularity quality assessment framework by comparing superior ground-truth charts with inferior Text2Vis outputs; and (2) VLM Fine-Tuning -- This knowledge is used to fine-tune a VLM through a two-stage approach, including warm-up and iterative preference alignment phases, to judge visualization quality and provide code-level refinement suggestions. Experimental results demonstrate that VisRef significantly outperforms state-of-the-art approaches, including LLM-based and VLM-prompted, and exhibits strong orthogonal compatibility with existing approaches.

Abstract In many real‐world applications, it is important for the authors to remove insignificant image details while preserving the significant structures. This is the so‐called image smoothing problem. In this paper, the authors investigate the image smoothing problem using the optimization model with . The authors employ the fast additive half‐quadratic (AHQ) iterative minimization algorithm for solving the optimization model. The authors discuss the convergence of the AQH iterative minimization algorithm. Experimental results and comparisons are provided to show the efficiency and flexibility of the proposed method in terms of both qualitative and quantitative evaluations.

This paper investigates the formation control problem for linear multi-agent systems under switching directed topologies. Based on absolute or relative outputs, we propose two distributed observer-based event-triggered control schemes. Both schemes can guarantee the boundedness of formation errors under sufficient conditions. The schemes can also avoid Zeno behaviors by giving an estimation for the lower bound of sampling intervals. Finally, simulations and experiments validate the proposed approaches.

Service optimization and energy conservation requires a thorough understanding of the performance impact of different hardware configurations. In this paper we focus on the configuration of memory and investigate the impact of memory dynamic voltage and frequency scaling (DVFS) on the performance of services/applications. We propose a quantitative metric called frequency sensitivity (FS) and study memory FS of various benchmarks. Our experiments yield several insights for memory DVFS based performance tuning.