Diffusion models have revealed powerful potential in all-in-one image restoration (AiOIR), which is talented in generating abundant texture details. The existing AiOIR methods either retrain a diffusion model or fine-tune the pretrained diffusion model with extra conditional guidance. However, they often suffer from high inference costs and limited adaptability to diverse degradation types. In this paper, we propose an efficient AiOIR method, Diffusion Once and Done (DOD), which aims to achieve superior restoration performance with only one-step sampling of Stable Diffusion (SD) models. Specifically, multi-degradation feature modulation is first introduced to capture different degradation prompts with a pretrained diffusion model. Then, parameter-efficient conditional low-rank adaptation integrates the prompts to enable the fine-tuning of the SD model for adapting to different degradation types. Besides, a high-fidelity detail enhancement module is integrated into the decoder of SD to improve structural and textural details. Experiments demonstrate that our method outperforms existing diffusion-based restoration approaches in both visual quality and inference efficiency.

In this article, a hybrid intelligent optimization method is proposed for the dynamic optimization of path-constrained switched systems with free switching sequences. This method combines improved particle swarm optimization and differential evolution (IPSO-DE) method with a gradient-based dynamic optimization method, which can simultaneously obtain the global optimal solution, i.e., optimal control input, optimal switching instants, and optimal switching sequences. First, control vector parameterization (CVP), switching time parameterization (STP), and switching sequence smoothing techniques are employed to transform the original problem into a continuous finite-dimensional dynamic one. Second, the path constraints are discretized into a finite number of point constraints, and the IPSO-DE algorithm is proposed to search for the global optimal solution of the continuous dynamic problem with discretized constraints. Then, the obtained optimal solution serves as the initial point to calculate the gradients of the objective function with respect to control input, switching instants, and switching sequences. Third, the gradient-based deterministic method is applied to obtain the global optimal solution that satisfies the first-order optimality condition. Fourth, the finite termination of the hybrid intelligent optimization method is proven. Finally, the effectiveness of the proposed method is verified through three numerical examples.

Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment, but overlook LLMs' inherent strength in natural language processing — their deep understanding of linguistic logic and structure rather than superficial embedding processing. We propose Context-Alignment (CA), a new paradigm that aligns TS with a linguistic component in the language environments familiar to LLMs to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities. Specifically, such context-level alignment comprises structural alignment and logical alignment, which is achieved by Dual-Scale Context-Alignment GNNs (DSCA-GNNs) applied to TS-language multimodal inputs. Structural alignment utilizes dual-scale nodes to describe hierarchical structure in TS-language, enabling LLMs to treat long TS data as a whole linguistic component while preserving intrinsic token features. Logical alignment uses directed edges to guide logical relationships, ensuring coherence in the contextual semantics. Following the DSCA-GNNs framework, we propose an instantiation method of CA, termed Few-Shot prompting Context-Alignment (FSCA), to enhance the capabilities of pre-trained LLMs in handling TS tasks. FSCA can be flexibly and repeatedly integrated into various layers of pre-trained LLMs to improve awareness of logic and structure, thereby enhancing performance. Extensive experiments show the effectiveness of FSCA and the importance of Context-Alignment across tasks, particularly in few-shot and zero-shot forecasting, confirming that Context-Alignment provides powerful prior knowledge on context. The code is open-sourced at https://github.com/tokaka22/ICLR25-FSCA.

Clouds and their shadows hinder accurate analysis of optical remote sensing imagery, making cloud removal an indispensable preprocessing step in remote sensing. However, existing methods lack efficient long-range modeling capabilities and overlook the impact of cloud irregularities and uncertainties on cloud removal. Additionally, resolving spectral confusion between cloud shadows and surface information remains a significant challenge. To tackle this issue, this study introduces an innovative cloud removal algorithm termed the multiscale hybrid sparse transformer (HSFormer), which adaptively removes clouds and shadows while reconstructing land surface semantics. HSFormer leverages pixel correlation explicit sparsity and uncertainty-driven implicit sparsity to maximize attention gains, enabling efficient cloud recognition and removal. The global pixel correlation based on attention relations enhances the semantic integrity of reconstructed images and avoids information loss across frequency domains. Furthermore, the uncertainty-guided adaptive receptive field enhances the model’s ability to resolve complex cloud-covered spatial relationships and reduces the spatial uncertainty of the reconstructed image. Experiments on simulated cloud shadow, real RICE, and full-band WHUS2-CRv datasets demonstrate HSFormer’s superiority over existing methods, improving PSNR and SSIM by 0.49% and 0.62%, respectively, in average evaluations across all bands of the WHUS2-CRv dataset and effectively addresses spectral aliasing between cloud shadows and dark surfaces.

Adversarial training is often formulated as a min-max problem, however, concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model, i.e., previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We characterize such non-ignorable samples as “hiders”, which reveal the hidden high-risk regions within the secure area obtained through adversarial training and prevent the model from finding the real worst cases. We demand the model to prevent hiders when defending against adversarial examples for improving accuracy and robustness simultaneously. By rethinking and redefining the min-max optimization problem for adversarial training, we propose a generalized adversarial training algorithm called Hider-Focused Adversarial Training (HFAT). HFAT introduces the iterative evolution optimization strategy to simplify the optimization problem and employs an auxiliary model to reveal hiders, effectively combining the optimization directions of standard adversarial training and prevention hiders. Furthermore, we introduce an adaptive weighting mechanism that facilitates the model in adaptively adjusting its focus between adversarial examples and hiders during different training periods. We demonstrate the effectiveness of our method based on extensive experiments, and ensure that HFAT can provide higher robustness and accuracy.

Blind super-resolution (SR) aims to restore real lowresolution (LR) images. However, most current methods focus on global uniform blur but neglect motion blur, and the few motion deblurring SR methods tend to produce too smooth images. In this letter, we introduce a novel diffusion-based SR method, which can effectively handle the motion blur effect in LR images and retain fine-grained texture information. Our method uses a deblurred feature extraction module and a texture feature extraction module to obtain deblurred features and texture features of the LR image respectively. These two features are then fed into the diffusion model, which samples the image from a learned distribution and outputs a clear and realistic HR image. Moreover, to speed up the sampling process of the diffusion model, we combine it with a conditional generative adversarial network (GAN) to implement stride sampling. Extensive experiments show that our method outperforms state-ofthe-art methods in terms of perceptual metrics, and can generate more natural and realistic images. The code is available athttps://github.com/tonia86/motion-blur-SR.

Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected researchers' attention towards the exploration of phone-based ambient temperature estimation methods. However, existing phone-based methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space. This system also provides an efficient, cost-effective approach with a few-shot meta-learning module and an automated label generation module. It shows that with just 5 new training data points, the temperature estimation model can adapt to a new phone and reach a good performance. Moreover, the system uses crowdsourcing to generate accurate labels for all newly collected training data, significantly reducing costs. Additionally, we highlight the potential of incorporating federated learning into our system to enhance privacy protection. We believe this study can advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.

Classical adversarial attacks for Face Recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA)11https://github.com/tokaka22/GMAA to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality.

Geomechanical logs are of ultimate importance for subsurface description and evaluation, as well as for the exploration of underground resources, such as oil and gas, groundwater, minerals, and geothermal energy. Together with geological and hydrological properties, low-cost and high-accuracy models can be generated based on geomechanical parameters. However, it is challenging to directly measure geomechanical parameters, and they are usually estimated based on other measured quantities. For example, geomechanical logs may be obtained with certain empirical models from sonic logs together with prior information such as rock types, which are not readily available. Finding a way to directly estimate geomechanical logs based on easily available conventional well logs can result in significant cost savings and increased efficiency. In this article, we showed that deep learning via the long short-term memory network (LSTM) is effective in constructing an end-to-end model that takes the spatial dependence in well logs into consideration. We further proposed a physics-constrained LSTM, in which the physical mechanism behind the geomechanical parameters is utilized as a priori information. This state-of-the-art model is capable to directly estimate geomechanical logs based on easily available data, and it achieves higher prediction accuracy since the domain knowledge of the problem is considered.

In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework. The ENN is also robust to small training data size because the ensemble of stochastic realizations essentially enlarges the training dataset. This constitutes a desirable characteristic, especially for real-world engineering applications. In addition, the ENN does not require the calculation of gradients, which enables the use of complicated neuron models and loss functions in neural networks. We experimentally demonstrate benefits of the proposed model, in particular showing that the ENN performs much better than the traditional Bayesian neural networks (BNN). The EnRML in ENN is a substitution of gradient-based optimization algorithms, which means that it can be directly combined with the feed-forward process in other existing (deep) neural networks, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), broadening future applications of the ENN.

In this letter, we propose a new graph cuts multi-frame super resolution method. The method is carried out in 3 steps. First, we project each high-resolution pixel p onto the low-resolution images and select low-resolution pixels which fall within the zone of influence of p. Second, we weigh the contribution of the low-resolution pixels via a soft switching function and add them to construct a virtual low resolution pixel. The high resolution image is then recovered after minimizing a Maximum a posteriori Markov Random Field (MAP-MRF) energy function. This is done by approximating our energy function to make it graph representable and minimize it with a graph cuts α-expansion algorithm. Experimental results show that our approach outperforms state-of-the-art methods.

Modern petroleum reservoir simulation serves as a primary tool for quantitatively managing reservoir production and planning new fields. It involves repeatedly solving the Jacobian of a set of strong nonlinear partial differential equations governing the mass and energy conduction and conservation. Most of the existing reservoir simulators adopt iterative solver with multiple stages of preconditioners, in which the incomplete LU (ILU) factorization is an outstanding universal smoother. However, it turns out that when the degree of freedom of each grid grows, ILU usually becomes the bottleneck of the solver. Moreover, ILU is difficult to parallelize due to its inherent data dependency. In this paper, we developed a sparse iterative solver with parallelized ILU and triangular solve using block-wise data structure. Compared with the state of art iterative solver on 14 industrial reservoir simulation matrices, the proposed ILU is 5.2x faster (on average) than the state of art iterative solver because of the block-wise data structure, which leads to 2.2x speedup on the total solver runtime. In addition, parallel ILU and triangular solve are developed to further accelerate the solver. To tackle the strong data dependency in ILU and triangular solve, we first partition the algorithm into separated tasks and construct a data flow graph to represent the data dependency. Then, tasks are scheduled in parallel according to the topological order of the data flow graph. On an 8-thread multicore architecture, we achieved another 3.6x speedup on ILU factorization, and 3.3x on triangular solve with good scalability.