In remote sensing image pansharpening, the fundamental objective is to generate high-resolution multispectral (HRMS) image that preserves spectral integrity while enhancing spatial resolution. However, contemporary approaches lack the ability to perceive the quality of input images, thereby failing to preserve desired performance in complex real-world scenarios characterized by panchromatic (PAN) image degradation (e.g., noise contamination or sensor limitations). To address this problem, we present a novel degradation-aware Taylor expansion approximation (DTEA) network for pansharpening, where DTEA includes the following key procedures: Firstly, the PAN image is hierarchically decomposed into feature maps to represent the degradation information through a proposed Taylor expansion approximation network (TEANet). Next, a multi-level information fusion network (MIFNet) is employed to integrate these feature maps with LRMS images, yielding fused maps with rich spatial and spectral information. Finally, the fused map from each layer is utilized to synthesize the desired HRMS image through inverse Taylor expansion, thereby overcoming diverse information degradation. To rigorously evaluate the effectiveness of our DTEA network, we conduct a systematic performance analysis across PAN images with diverse qualities. Extensive quantitative and qualitative experiments on three datasets demonstrate that our method outperforms state-of-the-art approaches while exhibiting excellent generalization capability in real-world scenarios. Source code will be made publicly available on https://github.com/MysterYxby/DTEA.

Aortic dissection (AD) is a life-threatening condition caused by a tear in the aortic intima, allowing blood to enter the vessel wall and form a false lumen. Due to its high mortality rate, timely diagnosis and precise treatment are critical. Clinical diagnosis and treatment of AD rely heavily on accurate 3D vascular image segmentation. To address existing methods’ low segmentation accuracy and insufficient geometric detail preservation, this paper proposes a Transformer-based Dual-Branch Mesh Segmentation Network (TD-MSeg) for AD. This network employs a mesh-based self-attention mechanism to retain vascular geometric details while adopting a dual-branch decoder to effectively fuse features and model long-range dependencies. Specifically, TD-MSeg incorporates three key components: a Hierarchical Mesh Transformer (HMT) module that enhances feature modeling of critical anatomical structures (e.g., intimal tears), a dual-branch decoder that facilitates collaborative optimization of multi-scale local and global features, and a mesh label refinement module that uses a wide-path exploration algorithm to eliminate deformation artifacts and improve spatial label continuity. Moreover, experiments on two AD mesh segmentation datasets demonstrate that the proposed TD-MSeg achieves a 6% improvement in accuracy compared to traditional models and significantly enhances the recognition of complex vascular structures, thereby providing high-precision 3D reconstruction support for endovascular surgical planning.

Crop growth prediction can help agricultural workers to make accurate and reasonable decisions on farming activities. Existing crop growth prediction models focus on one crop and train a single model for each crop. In this article, we develop a ubiquitous growth prediction model for multiple crops, aiming at training a single model for multiple crops. A ubiquitous vision and sensor transformer (ViST) model for crop growth prediction with image and sensor data is developed to achieve the goals. In the proposed model, a cross-attention mechanism is proposed to facilitate the fusion of multimodal feature maps to reduce computational costs and balance the interactive effects among features. To train the model, we combine the data from multiple crops to create a single (ViST) model. A sensor network system is established for data collection on the farm where rice, soybean, and maize are cultivated. Experimental results show that the proposed ViST model has an excellent ubiquitous ability for crop growth prediction with multiple crops.

Nowadays, Deep Learning-as-a-Service can be de-ployed in the Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has re-vealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in defending DNN against these attacks is to efficiently identify and filtering out the adversarial pixels. The state-of-the-art defense strategies with good robustness often require additional model training for specific attacks. To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erro-neous outputs, without a-priori knowledge about the attack type. We evaluated our progressive defense strategy against various attack methods on two well-known datasets. Experimental result shows it outperforms the state-of-the-art methods(Adversarial-PGD, Adversarial-Network, and Adversarial-Dual-Network) with dramatically reduced computation cost.

The increasing communication demands in chip multiprocessors (CMPs) and many error-tolerant applications are driving the approximate design of the network-on-chip (NoC) for power-efficient packet delivery. However, current approximate NoC designs achieve improvements in network performance or dynamic power savings at the cost of additional circuit design and increased area overhead. In this paper, we propose a novel approximate multiplane NoC (AMNoC) that provides low-latency transfer for latency-sensitive packets and minimizes the power consumption of approximable packets through a lossy bufferless subnetwork. The AMNoC also includes a regular buffered subnetwork to guarantee the lossless delivery of nonapproximable packets. Evaluations show that, compared with a single-plane buffered NoC, the AMNoC reduces the average latency by 41.9%. In addition, the AMNoC achieves 48.6% and 53.4% savings in power consumption and area overhead, respectively.

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input pixels. These pixels have to be filtered out for defending DNN that will cause lots of computations. To reduce the computation, in this paper, the pixels that their slight change will cause the neural network to have a wrong result for prediction are found out and filtered out for defense goal. Experiment results show that our defense method has achieved about 85% defense success rate with filtering out 50 mutation pixels.

Direct policy search is one of the most important algorithm of reinforcement learning. However, learning from scratch needs a large amount of experience data and can be easily prone to poor local optima. In order to overcome these challenges, this paper proposed a training-free behavior cloning algorithm called Policy Learning based on Completely Behavior Cloning (PLCBC). PLCBC transforms the Model Predictive Control (MPC) controller into a PieceWise Affine (PWA) function with multi-parametric programming, and uses a neural network to express this function. By this way, off-the-shelf deep reinforcement learning algorithms can be used to fine-tune this neural network. The experiments show that our method can help agent learn at the high reward state region, and converge faster and better.

Next-generation sequencing (NGS) and the third generation sequencing (TGS) have recently allowed us to develop algorithms to quantitatively dissect the extent of heterogeneity within a tumour, resolve cancer evolution history and identify the somatic variations and aneuploidy events. Simulation of tumor NGS and TGS data serves as a powerful and cost-effective approach for benchmarking these algorithms, however, there is no available tool that could simulate all the distinct subclonal genomes with diverse aneuploidy events and somatic variations according to the given tumor evolution history. We provide a simulation package, Pysubsim-tree, which could simulate the tumor genomes according to their evolution history defined by the somatic variations and aneuploidy events. Pysubsim-tree is free, open source, available at: https://github.com/dustincys/pysubsimtree.

Traffic regulation is an essential technology of networks-on-chip (NoC) to achieve communication performance guarantees with effective use of the system interconnect and low traffic delay. This paper presents approximation-based dynamic traffic regulation (ABDTR), which approximates part of traffic data instead of network transmission for mitigating network congestion based on the inherent error resilience of some applications. ABDTR drops a fraction of safe-to-approximate packet data that may aggravate network congestion before they are injected into network, and predicts the lost data in packet after being received in destination node. Traffic drop rate is fully adaptive to the traffic and network states. The regulation range of drop rate is a knob to control the trade-off between performance/energy efficiency and output quality. The proposed method is effective and can be implemented in hardware with small area. Experimental results have confirmed that the ABDTR can help reduce up to 44.4 percent average network delay with less than 10% loss in quality. The results also show ABDTR yields significant application speedup and NoC energy reduction for a wide range of quality-loss levels.