Hardware accelerators such as GPUs, NPUs, and FPGAs are essential to meeting AI’s computational demands. With the proliferation of heterogeneous devices across cloud and edge, various model optimization techniques adapt to diverse hardware characteristics through operator transformations and structural modifications. Accurate, efficient latency prediction enables rapid selection of optimal strategies across hardware backends. Many existing methods treat hardware as a black-box executor, directly regressing latency without explicitly modeling the intricate interactions between neural network (NN) structures and device-specific execution behaviors. To address these challenges, we introduce a new modeling perspective that captures the interaction between neural architectures and hardware execution. To capture device-specific characteristics, we propose two complementary modeling strategies. The Device Behavior Signature Selector (DBSel) characterizes hardware execution behavior by selectively probing a small set of representative architectures, forming a compact, workload-driven profile. In parallel, we construct capability vectors that capture the hierarchical memory of each device and compute characteristics, providing a structured abstraction of its architectural capacity. To unify both behavioral and structural views, we introduce the Hardware–Operation Dialogue Module (HODM), which models fine-grained interactions between neural operators and hardware properties. Together, these components empower CloserToMe to deliver accurate and transferable latency predictions across unseen and diverse platforms.

In real-time embedded systems, traditional timer based time-triggered preemptive scheduling suffers from priority inversion, where the release of a lower-priority task at its activation time interferes with the execution of a higher-priority task. This not only complicates the worst-case response time (WCRT) analysis for higher-priority tasks but also increases their actual WCRT, thereby degrading predictability and schedulability. Addressing this challenge requires enabling the processor to aware the priority of tasks reaching their activation time relative to the currently executing task and to make scheduling decisions accordingly. Since the instruction set serves as the software/hardware interface, the most direct approach to support this capability is to incorporate timing- and priority-aware semantics into the instruction set architecture. In this paper, we propose a novel instruction set, the Time-Triggered Instruction Set (TTI), which introduces priority-aware timed operations to allow the processor to perform operations based on time and priority. We then design a TTI-supported hardware microarchitecture. And we develop the TTI-based priority-inversionfree time-triggered preemptive scheduling. Experimental results demonstrate that, compared to timer-based scheduling, TTIbased scheduling achieves lower and more stable WCRT for high-priority tasks, improving predictability and schedulability.

The deep integration of 5G and the Internet of Things has spurred a pressing demand for high-precision indoor positioning in scenarios such as industrial automation and smart healthcare. However, traditional fingerprinting-based positioning techniques face significant constraints, including multipath signal degradation and insufficient adaptability, especially in challenging outdoor-to-indoor scenarios that rely on signals from external base stations. Concurrently, existing deep learning methods face challenges such as high annotation costs and limited model receptive fields. To address these limitations, this paper proposes an intelligent fingerprinting positioning system specifically designed to achieve robust indoor positioning using 5G signals from a single outdoor base station. By innovating in data preprocessing, feature matrix optimization, and an attention-enhanced model, this system aims to overcome the bottlenecks of positioning in such complex environments. Experimental results based on measured data from a real-world office building validate the superior performance of the proposed positioning system, achieving a mean absolute error (MAE) of 1.5442 m, which represents a significant improvement over state-of-the-art benchmarks.

Recent advancements in diffusion frameworks have significantly enhanced video editing, achieving high fidelity and strong alignment with textual prompts. However, conventional approaches using image diffusion models fall short in handling video dynamics, particularly for challenging temporal edits like motion adjustments. While current video diffusion models produce high-quality results, adapting them for efficient editing remains difficult due to the heavy computational demands that prevent the direct application of previous image editing techniques. To overcome these limitations, we introduce FADE—a training-free yet highly effective video editing approach that fully leverages the inherent priors from pre-trained video diffusion models via frequency-aware factorization. Rather than simply using these models, we first analyze the attention patterns within the video model to reveal how video priors are distributed across different components. Building on these insights, we propose a factorization strategy to optimize each component’s specialized role. Furthermore, we devise spectrum-guided modulation to refine the sampling trajectory with frequency domain cues, preventing information leakage and supporting efficient, versatile edits while preserving the basic spatial and temporal structure. Extensive experiments on real-world videos demonstrate that our method consistently delivers high-quality, realistic and temporally coherent editing results both qualitatively and quantitatively. Code is available at https://github.com/EternalEvan/FADE.

Joint reconstruction of human-object interaction marks a significant milestone in comprehending the intricate interrelations between humans and their surrounding environment. Nevertheless, previous optimization methods often struggle to achieve physically plausible reconstruction results due to the lack of prior knowledge about human-object interactions. In this paper, we introduce ScoreHOI, an effective diffusion-based optimizer that introduces diffusion priors for the precise recovery of human-object interactions. By harnessing the controllability within score-guided sampling, the diffusion model can reconstruct a conditional distribution of human and object pose given the image observation and object feature. During inference, the ScoreHOI effectively improves the reconstruction results by guiding the denoising process with specific physical constraints. Furthermore, we propose a contact-driven iterative refinement approach to enhance the contact plausibility and improve the reconstruction accuracy. Extensive evaluations on standard benchmarks demonstrate ScoreHOI's superior performance over state-of-the-art methods, highlighting its ability to achieve a precise and robust improvement in joint human-object interaction reconstruction.

Image enhancement finds wide-ranging applications in real-world scenarios due to complex environments and the inherent limitations of imaging devices. Recent diffusion-based methods yield promising outcomes but necessitate prolonged and computationally intensive iterative sampling. In response, we propose InstaRevive, a straightforward yet powerful image enhancement framework that employs score-based diffusion distillation to harness potent generative capability and minimize the sampling steps. To fully exploit the potential of the pre-trained diffusion model, we devise a practical and effective diffusion distillation pipeline using dynamic noise control to address inaccuracies in updating direction during score matching. Our noise control strategy enables a dynamic diffusing scope, facilitating precise learning of denoising trajectories within the diffusion model and ensuring accurate distribution matching gradients during training. Additionally, to enrich guidance for the generative power, we incorporate textual prompts via image captioning as auxiliary conditions, fostering further exploration of the diffusion model. Extensive experiments substantiate the efficacy of our framework across a diverse array of challenging tasks and datasets, unveiling the compelling efficacy and efficiency of InstaRevive in delivering high-quality and visually appealing results.

Real-Time Embedded Systems (RTES) are widely used in safety-critical devices, where deterministic data flow is essential to system verification and reliable execution. It requires that each consumer task instance reads data from the deterministic producer task instance. In software based on general-purpose computing instruction sets, communication-related instruction execution order couples data flow among tasks, necessitating a deterministic execution order of these instructions to preserve data-flow determinism. However, enforcing this order complicates software, and suffers from priority inversion and variable execution overheads, which significantly increases task worst-case response times (WCRT) and response time variability. This paper identifies the cause of above issues as the semantics of general-purpose instruction sets, under which dataflow determinism relies on the deterministic execution order of communication-related instructions. To address this, we make the following contributions. First, we propose Time-Semantic Instruction set (TSI), which supports memory access using both addresses and timestamps. TSI enables data-flow determinism without strict instruction ordering. Second, we design a TSI-enabled implementation compatible with conventional memory systems. Third, we provide two TSI-based deterministic data-flow programming paradigms, along with correctness proofs. Finally, we evaluate TSI hardware cost and implement a cycle-accurate simulator based on a TSI-extended RISC-V. Experiments demonstrate that, under reasonable memory overhead, our approach reduces programming complexity and achieves up to$21.6 \times$reduction in WCRT and up to$89.6 \times$reduction in response time variability compared to existing methods.

Heterogeneous systems commonly adopt dynamic scheduling algorithms to improve resource utilization and enhance scheduling flexibility. However, it may introduce timing anomalies, wherein locally reduced tasks' actual execution times can lead to an increase in the overall system execution time. This phenomenon significantly complicates the analysis of WorstCase Response Time (WCRT), rendering conventional analysis either overly pessimistic or unsafe, and often necessitating exhaustive state-space exploration to ensure correctness. To address this challenge, this paper presents the first timing-anomalyfree dynamic scheduling algorithm for heterogeneous systems, referred to as Deterministic Dynamic Execution. The core idea is to apply deterministic execution constraints, which partially restrict the resource allocation and execution order of tasks at runtime. It achieves a safe and tight WCRT through a single offline simulation execution. In this paper, we provide preliminary experimental validation of the timing-anomaly-free property of our algorithm and outline the basic idea of a formal proof.

Interaction is crucial for online self-regulated learning (OSRL) ability and learning outcomes. The absence of social interaction might lead to high dropout rates in online learning environments. Danmaku holds great potential to enhance online learning by fostering interaction. This study explores the motivations behind university students’ use of danmaku and its influence on their OSRL. Using a mixed methods approach through surveys and interviews with 100 university students from two universities, we found that danmaku promotes social interaction by fulfilling students’ information and entertainment needs. Additionally, engagement with danmaku supports self-regulated learning through reflection and responding strategies and enhances enjoyment by increasing self-efficacy in contributing to the online learning community. This study expands understanding of the role interactive tools like danmaku can play in enhancing social interaction and OSRL, and highlights the potential of danmaku to improve student engagement and reduce dropout rates for quality education.

Building on the success of universal language models in natural language processing (NLP), researchers have recently sought to develop methods capable of tackling a broad spectrum of visual tasks within a unified foundation framework. However, existing universal vision models face significant challenges when adapting to the rapidly expanding scope of downstream tasks. These challenges stem not only from the prohibitive computational and storage expenses associated with training such models but also from the complexity of their workflows, which makes efficient adaptations difficult. Moreover, these models often fail to deliver the required performance and versatility for a broad spectrum of applications, largely due to their incomplete visual generation and perception capabilities, limiting their generalizability and effectiveness in diverse settings. In this paper, we present VisionHub, a novel universal vision model designed to concurrently manage multiple visual restoration and perception tasks, while offering streamlined transferability to downstream tasks. Our model leverages the frozen denoising U-Net architecture from Stable Diffusion as the backbone, fully exploiting its inherent potential for both visual restoration and perception. To further enhance the model's flexibility, we propose the incorporation of lightweight task-plugins and the task router, which are seamlessly integrated onto the U-Net backbone. This architecture enables VisionHub to efficiently handle various vision tasks according to user-provided natural language instructions, all while maintaining minimal storage costs and operational overhead. Extensive experiments across 11 different vision tasks showcase both the efficiency and effectiveness of our approach. Remarkably, VisionHub achieves competitive performance across a variety of benchmarks, including 53.3% mIoU on ADE20K semantic segmentation, 0.253 RMSE on NYUv2 depth estimation, and 74.2 AP on MS-COCO pose estimation.

Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clustering sce-narios. However, we discover that camera variation has a significant negative impact on the reliability of Jaccard distance. In particular, Jaccard distance calculates the distance based on the overlap of relevant neighbors. Due to camera variation, intra-camera samples dominate the rele-vant neighbors, which reduces the reliability of the neigh-bors by introducing intra-camera negative samples and ex-cluding inter-camera positive samples. To overcome this problem, we propose a novel camera-aware Jaccard (CA-Jaccard) distance that leverages camera information to en-hance the reliability of Jaccard distance. Specifically, we design camera-aware k-reciprocal nearest neighbors (CK-RNNs) to find k-reciprocal nearest neighbors on the intra-camera and inter-camera ranking lists, which improves the reliability of relevant neighbors and guarantees the con-tribution of inter-camera samples in the overlap. More-over, we propose a camera-aware local query expansion (CLQE) to mine reliable samples in relevant neighbors by exploiting camera variation as a strong constraint and as-sign these samples higher weights in overlap, further im-proving the reliability. Our CA-Jaccard distance is simple yet effective and can serve as a general distance metric for person re-ID methods with high reliability and low computational cost. Extensive experiments demonstrate the ef-fectiveness of our method. Code is available at https://github.com/chen960/CA-Jaccard/.

The recovery of occluded human meshes presents challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper, we introduce DPMesh, an innovative framework for occluded human mesh recovery that capitalizes on the pro-found diffusion prior about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model. Unlike previous methods reliant on conventional backbones for vanilla feature extraction, DPMesh seamlessly integrates the pre-trained denoising U-Net with potent knowledge as its image backbone and performs a single-step inference to provide occlusion-aware information. To enhance the perception capability for occluded poses, DPMesh incorporates well-designed guidance via condition injection, which produces effective controls from 2D observations for the denoising U-Net. Furthermore, we explore a dedicated noisy key-point reasoning approach to mitigate disturbances arising from occlusion and crowded scenarios. This strategy fully unleashes the perceptual capability of the diffusion prior, thereby enhancing accuracy. Extensive experiments affirm the efficacy of our frame-work, as we outperform state-of-the-art methods on both occlusion-specific and standard datasets. The persuasive results underscore its ability to achieve precise and robust 3D human mesh recovery, particularly in challenging scenarios involving occlusion and crowded scenes. Code is available at https://github.com/EternalEvan/DPMesh.

Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices. Conventional methods are often constrained by their tailored models, resulting in diminished robustness when confronted with challenging degradation conditions. In response, we propose FlowIE, a simple yet highly effective flow-based image enhancement framework that estimates straight-line paths from an elementary distribution to high-quality images. Unlike previous diffusion-based methods that suffer from long-time inference, FlowIE constructs a linear many-to-one transport mapping via conditioned rectified flow. The rectification straightens the trajectories of probability transfer, accelerating inference by an order of magnitude. This design enables our FlowIE to fully exploit rich knowledge in the pretrained diffusion model, rendering it well-suited for various real-world applications. Moreover, we devise a faster inference algorithm, inspired by Lagrange's Mean Value Theorem, harnessing midpoint tangent direction to optimize path estimation, ultimately yielding visually superior results. Thanks to these designs, our FlowIE adeptly manages a diverse range of enhancement tasks within a concise sequence of fewer than 5 steps. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets, unveiling the compelling efficacy and efficiency of our proposed FlowIE. Code is available at https://github.com/EternalEvan/FlowIE.

Shared last-level caches in multi-core architectures may lead to mutual interference in memory accesses between cores, resulting in additional access latency. To ensure the accuracy of programs' worst-case execution time (WCET) analysis, the inter-core interference of shared caches must be analyzed. This involves getting the time when memory access may occur between cores (i.e., inter-core context). But due to the uncertainty of inputs, describing inter-core context becomes extremely challenging. Existing works often consider the entire execution time of program as the time when all memory accesses may occur to enhance scalability, which ignores the timing information within programs and may lead to an overestimation of interference. This paper proposes a fine-grained shared cache interference analysis method based on the execution time of basic blocks. It employs a path-based strategy to estimate the execution time of basic blocks and use it as the lifecycles of memory accesses within the blocks, which can effectively eliminate impossible interferences. Experiments show that compared to the all interference method, we can reduce WCET by 65% in the best case and by 14% on average, with only a 130% increase in average analysis time.

Person-agnostic face swapping has gained significant attention in recent years, as it offers the potential to enhance various real-world applications by combining high fidelity and identity consistency. However, conventional face swapping methods often rely on intricate adjustments of different loss functions, leading to instability during both the training and inference stages. In this work, we propose a simple yet effective framework namedStableSwapwith a reversible autoencoder to modify the face in a shared latent space. Our approach capitalizes on the information-rich image latent codes to tackle the challenges of complex editing tasks, utilizing the abundant details present in both the source and target faces. To ensure an expressive and robust latent space, we employ a latent alignment approach with perceptual and adversarial losses to optimize the autoencoder. Additionally, we devise a multi-stage identity injection module that samples multiple features with different facial priors and incorporates them to guide the latent image manipulation. By leveraging attention-based blocks, we fuse these futures and update the latent code in a mask-conditioned manner. Both quantitative and qualitative results on the mainstream benchmarks demonstrate that our StableSwap generates competitive identity-consistent swapped faces compared with state-of-the-art methods. Our method outperforms previous approaches in terms of ID Retrieval (98.68) and FID (2.49), while also exhibiting enhanced stability during model training. Beyond this, our model achieves region-controllable face swapping with the capability to perform more fine-grained operations in latent space.

With the advancement of geo-systems and the increased availability of satellite data, a plethora of Land-Use and Land-Cover (LULC) products have been developed. The existing LULC products primarily relied on time-series imagery to classify land by pixel-based classifiers, allowing for local analysis and accurate boundary detection. However, the advent of deep learning has shifted towards the use of patch-based CNN models for generating land cover maps. In this paper, (1) we create a training dataset for China using a voting strategy based on three off-the-shelf available LULC products, avoiding the labor-intensive manual annotation. (2) We design a novel CNN-based model for LULC task, called Multi-modal Fine-grained Dual Network (dubbed as Dual-Net), which takes dual-date images to generate final maps, and reduces the need for gap-free temporal sequences or separate cloud detection. To leverage the correlation between location, date, and category, we embed multi-modal information (dates and geo-locations) to the model. Further, by incorporating low-level constraints and using pseudo-label refinement, we continually improve the performance and achieve more refined segmentation. (3) Due to the lack of a suitable validation dataset for China, we create a new validation dataset called China Sentinel2 Validation Dataset (CSVD) by manually annotating 733 finely labeled images of 1024 × 1024 pixels of China-specific Sentinel2 data. (4) Extensive experiments demonstrate that our model outperforms existing LULC products and produces more fine-grained segmentation results comparable to other patch-based products. Finally, we release annual LULC maps for China in 2020-2022 and also make our model accessible online for real-time results export.

Color is one of the fundamental elements of paintings. This paper proposes a set of measurements for color usage in a painting, including basic color elements, color harmony templates, and spatial distribution, characterizing both global and local features of color. Applying the measurements to over 3000 abstract paintings, oil paintings and Chinese ink paintings, we are able to observe the roles of color in the three genres of paintings. We report our findings in details on the effectiveness of these measurements, which may serve as tools for classification of paintings. The work is the first of this kind and points to further investigation of color usage in other forms of art and design.

Stalling from the thinking about the representation of geo-spatial data in computer network, the challenge of grid computing environment to geo-spatial information science and technology is pointed in This work. After review and summation of the achieved progress and existing problems of geo-spatial information systems in the past 20 years, the authors propose a new representation method for spatial data and spatial information - the spatial information multi-grid (SIMG), which can not only easily run under grid computing environment, but also properly consider the difference of natural and social characteristics in Earth space as well as the different level of economical development in different area. The system structure, data representation, data storage and data access in SIMG are described with emphasis on key techniques. The data conversion and transferring between SIMG and conventional spatial databases are also discussed. The applicability of SIMG in global, national, provincial and local decision-making is briefly indicated.