Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep learning have undeniably brought about substantial performance improvements in these fields. Nevertheless, existing methods mainly rely on task-specific architectures. This significantly restricts their generalization ability and effective application across a wide range of different tasks. In this paper, we introduce the Unified Rectification Framework (UniRect), a comprehensive approach that addresses these practical tasks from a consistent distortion rectification perspective. Our approach incorporates various task-specific inverse problems into a general distortion model by simulating different types of lenses. To handle diverse distortions, UniRect adopts one task-agnostic rectification framework with a dual-component structure: a Deformation Module, which utilizes a novel Residual Progressive Thin-Plate Spline (RP-TPS) model to address complex geometric deformations, and a subsequent Restoration Module, which employs Residual Mamba Blocks (RMBs) to counteract the degradation caused by the deformation process and enhance the fidelity of the output image. Moreover, a Sparse Mixture-of-Experts (SMoEs) structure is designed to circumvent heavy task competition in multi-task learning due to varying distortions. Extensive experiments demonstrate that our models have achieved state-of-the-art performance compared with other up-to-date methods.

Vanilla convolution and window-based self-attention have shown significant success in image dehazing. However, they are constrained by limited receptive fields and ignore frequency gaps between dehazed and clear images. The former hampers the modeling of global dependencies, while the latter impedes the learning of high-frequency features, leading to suboptimal performance. In this paper, we propose the Joint Spatial and Fourier Convolutional Network (JSFC-Net), which leverages Fourier transformation to simultaneously address the two aforementioned problems with low computational overhead. We introduce the Frequency-Spatial Promoted and Physical Learning Block, which extracts high-level features from the spatial domain and frequency domain in parallel. We design a simple yet effective solution that uses spatial features to promote and modulate frequency features in a multi-scale manner, achieving refinement of frequency features and addressing robustness issue caused by global sensitivity. Additionally, we present the Receptive Field Selection Module to facilitate improved fusion of spatial and frequency domain features. Finally, we introduce frequency loss to further narrow frequency gaps. Comprehensive experiments on multiple datasets demonstrate that JSFC-Net is significantly superior to SOTA dehazing methods.

Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from health controls. Using plasma peptides, the neural network, termed as CMS-Net, can perform diagnosis and prediction with an accuracy of 0.9441. The sensitivity and specificity reached 0.9352 and 0.9517 respectively, and the area under the curve was enhanced to 0.9634. Using the gradient-based feature importance method to interpret crucial features, we identify 28 differential peptide sequences from 14 precursor proteins (e.g. hemoglobin, immunoglobulin, albumin, etc.). This work highlights the possibility of molecular diagnosis of MDD with the aid of chemical and computer science.

SAR-assisted thick cloud removal from optical remote sensing images has long been a challenging task. Current mainstream methods face challenges in achieving an effective global receptive field, fully utilizing multi-scale features, and deeply integrating features from both modalities. To overcome these limitations, we propose the Multi-scale Multi-branch Mamba model(M3-CR) for SAR-assisted thick cloud removal. Specifically, we integrate the Mamba model into the task of SAR-assisted cloud removal, effectively modeling global dependencies within the images. Concurrently, a Multi-scale Multi-branch structure is introduced to extract and integrate multi-scale information, and in combination with a convolutional branch to fully exploit the global and local geographic proximities inherent in remote sensing images. Furthermore, we present a novel feature fusion module leveraging the Modal-Traversing 2D Selective Scan(MTSS2D) to enable deep interaction and integration of features from optical and SAR images. The experimental results on two benchmark databases show that the M3-CR achieves superior performance compared to state-of-theart cloud removal approaches, while requiring fewer parameters and reduced FLOPs. The code for M3-CR will be made publicly available at https://github.com/LinpengPan/M3CR.

Existing methods for remote sensing image dehazing and thin cloud removal treat this image restoration task as a clear pixel estimation problem, yielding a single prediction result through a deterministic pipeline. However, image restoration is a highly ill-posed problem, as the sharp pixel value corresponding to the input cannot be uniquely determined solely from the degraded image. In this paper, we present a novel algorithm for haze and thin cloud removal using Conditional Variational Autoencoders (CVAE) to generate multiple realistic restored images for each input. By sampling from the latent space to capture the pixel diversity, the proposed method mitigates the limitations arising from inaccuracies in a single estimation. In this uncertainty pipeline, we can generate a more accurate restored image based on these multiple predictions. Furthermore, we have developed a Dynamic Fusion Network (DFN) for combining multiple plausible outcomes to obtain a more accurate result. DFN dynamically predicts the kernels used for restored result generation conditioned on inputs, improving haze and thin cloud thanks to its adaptive nature. Quantitative and qualitative experiments demonstrate that the proposed method outperforms existing state-of-the-art techniques by a significant margin on dehazing and thin cloud removal benchmarks.

Existing Transformer-based dehazing methods for remote sensing (RS) images, to avoid quadratic computation complexity with respect to the feature map size, either perform self-attention mechanisms within local windows or capture long-range dependencies in the channel dimension rather than spatial. Each of these methods has its drawbacks. To address these limitations, we propose the Proxy and Cross-Stripes Integration Transformer (PCSformer) for RS image dehazing. PCSformer introduces two innovative Transformer blocks, i.e., sliding cross-stripes Transformer block and local proxy-based global Transformer block. The former allows us to directly model long-range dependencies and capture rich contextual information for large-scale objects in RS images. The latter seeks valuable information for thick haze regions within the whole feature map, generating more consistent and realistic scene details for such regions. Both achieve a large receptive field with cost-effective computational complexity within a single Transformer block. Furthermore, we introduce a shallow deep model with a small receptive field to conduct local refinement, which can mitigate artifacts associated with a large receptive field. Finally, to facilitate the better application of dehazing models to downstream visual tasks, we contribute two large-scale datasets for RS image dehazing. Experiments indicate that the dehazing models trained on our datasets can better assist downstream visual tasks under hazy atmospheric conditions compared to the dehazing models trained on existing datasets. Quantitative and qualitative experiments demonstrate that the proposed PCSformer significantly outperforms existing state-of-the-art techniques on dehazing benchmarks, particularly excelling in the restoration of thick haze scenes. The code and datasets are available athttps://github.com/SmileShaun/PCSformer.

Zero-Shot Learning (ZSL) aims to recognize unseen classes that never appear during training. Recently, generative adversarial networks (GANs) have been introduced to convert ZSL into a supervised learning problem by synthesizing unseen visual features. However, since unseen classes are never experienced for the generator during training, the synthesized unseen visual features often become heavily biased towards seen classes, or sometimes there is even no meaningful class that can be assigned to them. This is known as thebias problem. In this paper, we propose a novel method, namely Adaptive Bias-Aware GAN (ABA-GAN), to alleviate generating biased visual features. For this purpose, we build a semantic adversarial network to regularize the feature generator. Specifically, an adaptive adversarial loss is proposed to constrain the feature distributions, which avoids the generation of meaningless visual features. Meanwhile, a domain divider is presented to explicitly distinguish synthesized visual features between seen and unseen domains, such that the bias towards seen classes can be alleviated. Moreover, we propose a novel metric named bias score (BS) to explicitly quantify the degree of the strong bias. Extensive experiments on four widely used benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art approaches under both ZSL and GZSL protocols.

Traditional BGP implementation is based on single process or single thread model and not fit for cluster architecture of future core router. We have developed SDBGP, a distributed BGP implementation for future core router that provides excellent performance, reliability and scalability. SDBGP is designed on a fully distributed architecture, which gives equal chance for router nodes to participate in BGP routes computing and storage. SDBGP distributes BGP neighbors among cluster router nodes in a balanced way and improves BGP's performance by parallel processing of BGP neighbors. We deploy SDBGP on a software cluster router with four nodes. Performance testing shows that SDBGP can achieve great scalability in neighbor number and routes computation. It can get almost linear speedup with the increasing of cluster route size.