The multispectral and hyperspectral image fusion tasks aim to obtain the hyperspectral image(HSI) with a high spatial resolution. However, existing fusion methods usually utilize degradation simulation for training due to the inaccessible ground truth, which inevitably leads to spatial distortion and parameter bias during application. Besides, neglect of the prior information inherent in inputs and the deep-learning network’s low-frequency preference eventually leads to lower interpretability and limited performance. To this end, we proposed a model-embedded two-stage diffusion method(MTDiff) for unsupervised reconstruction of high spatial resolution HSI. In the first stage, the degradation model is estimated by the prior information inherent input pairs. Meanwhile, in the second stage, embedded with the degradation model, the dual-resolution diffusion model reconstructs high-resolution HSI. Specifically, treating the degradation process as a fixed diffusion step, an unsupervised paradigm is established through a mapping from upsampled low-resolution HSI to high-resolution HSI. Besides, with the estimated degradation model, the well-designed dual-resolution diffusion model step-by-step perturbs and then denoises the image in both native and degraded resolutions for a fidelity reconstruction with great interpretability. Furthermore, to establish a high-frequency shortcut for network learning, a discrete cosine injection module is designed to flatten the frequency information to a clear 2D domain with a huge high-frequency area, achieving sharp textures and clear structures in fusion results. Extensive systematic experiments across three datasets indicate the superior performance of MTDiff in multispectral and hyperspectral fusion tasks.

A parallel task can always be modelled as a directed acyclic graph (DAG), where sequential instruction blocks are modelled as vertices and data dependencies or resource constraints are modelled as edges. We propose a new federated scheduling algorithm for arbitrary-deadline sporadic DAG tasks, assuming that the exact structures of DAG tasks are unknown before runtime. Federated scheduling algorithms are a class of algorithms that can efficiently schedule DAG tasks by assigning several processors exclusively to each task. Existing studies have shown the advantages of federated scheduling, which include increasing the analytical schedulability and minimising the scheduling overhead. We are particularly focused on the scheduling of any task with a deadline longer than its release period; in this case, multiple jobs generated by the task could run concurrently. For such tasks, our algorithm is different from most federated scheduling algorithms in that it assigns dedicated processors to each job instead of letting jobs released by the same task share processors. The main idea is to increase the analytical schedulability by avoiding interference between jobs. The simulation results show that our algorithm outperforms existing algorithms when the exact structures of tasks are unknown before runtime.

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of 1/2(1++((1+)24/m)) when scheduling multiple DAG tasks with constrained deadlines, in which m is the number of processors and is the maximum ratio of task period to deadline. This value is lower than the current best result +2((1+-1/m)(1-1/m)). We also prove that a capacity augmentation bound of 1/2(1+2+((1+2)242/m)) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets.

Co-saliency detection aims to identify the common and salient objects from a group of relevant images. The main challenge for co-saliency detection is how to mine and exploit the saliency cues of both intra-image and inter-image. In this paper, we present a novel unified hierarchical neural network (UHGNN). We first construct the graph model by segmenting the images into super-pixels and extracting the intra-image hierarchical saliency cues. Then, the inter-image hierarchical saliency representation is mined to form the unified two-dimensional hierarchical feature setup. We further propose the geometric attention module to make the most of the intra-image and inter-image cues. Our UHGNN model competes or outperforms the state-of-the-art methods on two co-saliency detection benchmark datasets (MSRC, iCoSeg).

Various scheduling algorithms have been proposed for real-time parallel tasks modeled as a Directed Acyclic Graph (DAG). The capacity augmentation bound is a quantitative metric widely used in this field to compare the algorithms. Among the existing algorithms, the lowest capacity augmentation bound for DAG tasks with implicit deadlines is 2, which has been achieved by federated scheduling. To improve the schedulability and lower the capacity augmentation bound, this paper proposes DAG-Fluid, an algorithm based on fluid scheduling. We prove that DAG-Fluid has a capacity augmentation bound of 2 - 1/m+1, in which m is the number of processors in the system. Experiments show that DAG-Fluid performs better than the state of the art scheduling algorithms.