Transformer-based progressive residual network for single image dehazing

IntroductionThe seriously degraded fogging image affects the further visual tasks. How to obtain a fog-free image is not only challenging, but also important in computer vision. Recently, the vision transformer (ViT) architecture has achieved very efficient performance in several vision areas.MethodsIn this paper, we propose a new transformer-based progressive residual network. Different from the existing single-stage ViT architecture, we recursively call the progressive residual network with the introduction of swin transformer. Specifically, our progressive residual network consists of three main components: the recurrent block, the transformer codecs and the supervise fusion module. First, the recursive block learns the features of the input image, while connecting the original image features of the original iteration. Then, the encoder introduces the swin transformer block to encode the feature representation of the decomposed block, and continuously reduces the feature mapping resolution to extract remote context features. The decoder recursively selects and fuses image features by combining attention mechanism and dense residual blocks. In addition, we add a channel attention mechanism between codecs to focus on the importance of different features.Results and discussionThe experimental results show that the performance of this method outperforms state-of-the-art handcrafted and learning-based methods

3S-TSE: Efficient Three-Stage Target Speaker Extraction for Real-Time and Low-Resource Applications

Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient real-time target speaker extraction.Comment: Accepted to ICASSP 202

Late Cenozoic tectonic evolution of the Ailao Shan-Red River fault (SE Tibet): implications for kinematic change during plateau growth

Surface uplift, river incision, shear zone exhumation, and displacement along active faults have all interacted to shape the modern landscape in the southeastern margin of the Tibetan Plateau. The Ailao Shan-Red River fault, a major structure in the tectonic evolution of southeastern Asia, is an excellent recorder of these processes. We present new stratigraphic, structural, and low-temperature thermochronologic data to explore its late Cenozoic tectonic and geomorphic evolution. The stratigraphic and structural observations indicate that the major bend in the fault was a releasing bend with significant Miocene sedimentation in the early–middle Miocene but became a restraining bend with abundant shortening structures developed after the late Miocene reversal of displacement. We also document exhumation of the shear zone from two low-temperature thermochronologic transects. New apatite (U-Th)/He(AHe) data and published thermochronologic results reveal two accelerated cooling episodes, backed by stratigraphic and geomorphic observations. The first rapid cooling phase occurred from ca. 27 to 17 Ma with removal of cover rocks and exhumation of the shear zone. The second accelerated cooling episode revealed by our AHe data commenced at 14–13 Ma, lasting 2–3 Myr. The Ailao Shan range may have risen to its modern elevation with high-relief topography developing due to river incision. We interpret the onset of this rapid exhumation to reflect renewed plateau growth associated with lower crustal flow

Phase behavior of TPGS–PEG400/1450 systems and their application to liquid formulation: A formulation platform approach

Vitamin E d ‐alpha‐tocopheryl polyethylene glycol succinate (TPGS) and polyethylene glycol are common excipients used in both preclinical and commercial formulations. In this paper, the phase diagrams of TPGS and polyethylene glycol 400 (PEG 400) in the presence of either water or ethanol were constructed. The effect of water and ethanol on the cloud point temperature of TPGS–PEG 400 mixtures was investigated. In general, the cloud point temperature was reduced by the presence of either water or ethanol in the formulation. However, water was more effective in lowering the cloud point temperature than ethanol. Similarly, the phase diagram of TPGS–PEG 1450 was constructed. The cloud point temperature was observed to decrease with increasing TPGS concentration. It was found that TPGS and PEG 1450 could form a single phase when TPGS concentration was above 75%, based on differential scanning calorimetry, and FT‐Raman analysis indicated that a vibration at 1330 cm –1 disappeared in the melted single phase. In addition, a systematic melting point depression was observed for the mixtures of TPGS–PEG 1450. In the presence of Ibuprofen, a model compound, the cloud point temperature was also reduced. Finally, the extended Flory–Huggins theory for polymer solution was used to analyze the entropic and enthalpic contributions of water and ethanol to the free energy of mixing. © 2011 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:4907–4921, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87132/1/22659_ftp.pd

Development and internal validation of a nomogram to predict temporary acute agitated delirium after surgery for chronic subdural hematoma in elderly patients: an analysis of the clinical database

BackgroundThis study aimed to develop a nomogram for predicting temporary acute agitated delirium after surgery in patients with chronic subdural hematoma (CSH) without neurological compromise and hospitalized in the neurosurgery.MethodsWe included 289 patients with chronic subdural hematoma (CSH) from the medical information system of Yuebei People’s Hospital of Shaoguan City, Guangdong Province, and collected 16 clinical indicators within 24 h of admission. We used the least absolute shrinkage and selection operator (LASSO) regression to identify risk factors. We established a multivariate logistic regression model and constructed a nomogram. We performed internal validation by 1,000 bootstrap samples; we plotted a receiver operating curve (ROC) and calculated the area under the curve (AUC), sensitivity, and specificity. We also evaluated the calibration of our model by the calibration curve and the Hosmer–Lemeshow goodness-of-fit test (HL test). We performed a decision curve analysis (DCA) and a clinical impact curve (CIC) to assess the net clinical benefit of our model.ResultsThe nomogram included alcoholism history, hepatic insufficiency, verbal rating scale for postoperative pain (VRS), pre-hospital modified Rankin Scale (mRS), and preoperative hematoma thickness as predictors. Our model showed satisfactory diagnostic performance with an AUC value of 0.8474 in the validation set. The calibration curve and the HL test showed good agreement between predicted and observed outcomes (p = 0.9288). The DCA and CIC showed that our model had a high predictive ability for the occurrence of postoperative delirium in patients with CSDH.ConclusionWe identified alcoholism, liver dysfunction, pre-hospital mRS, preoperative hematoma thickness, and postoperative VRS pain as predictors of postoperative delirium in chronic subdural hematoma patients. We developed and validated a multivariate logistic regression model and a nomogram

Helical Luttinger liquid on the edge of a 2-dimensional topological antiferromagnet

Boundary helical Luttinger liquid (HLL) with broken bulk time-reversal symmetry belongs to a unique topological class which may occur in antiferromagnets (AFM). Here, we search for signatures of HLL on the edge of a recently discovered topological AFM, MnBi2Te4 even-layer. Using scanning superconducting quantum interference device, we directly image helical edge current in the AFM ground state appearing at its charge neutral point. Such helical edge state accompanies an insulating bulk which is topologically distinct from the ferromagnetic Chern insulator phase as revealed in a magnetic field driven quantum phase transition. The edge conductance of the AFM order follows a power-law as a function of temperature and source-drain bias which serves as strong evidence for HLL. Such HLL scaling is robust at finite fields below the quantum critical point. The observed HLL in a layered AFM semiconductor represents a highly tunable topological matter compatible with future spintronics and quantum computation

Two-phase exhumation along major shear zones in the SE Tibetan Plateau in the late Cenozoic

Three continent-scale shear zones are arguably the most outstanding structural features in the southeastern Tibetan Plateau, and therefore, their tectonic and landscape evolution have significant implications for understanding the history and mechanisms of intracontinental mountain building and plateau growth. This study presents low-temperature thermochronology from the Gaoligong and Chongshan shear zones (GLSZ and CSSZ) and quantitative analyses of fluvial longitudinal profiles of tributaries in the Salween drainage, which lies between the shear zones. Apatite and zircon (U-Th)/He data reveal a two-stage exhumation history for both shear zones: rapid and prominent cooling in the middle Miocene followed by a second, lower magnitude cooling event in the late Miocene to early Pliocene. Ductile transpressional shearing is inferred to have caused the first cooling, continuing until ~11\ua0Ma. The northward migration of the tectonic events along the Mogok metamorphic belt and GLSZ and synchronous dextral displacement along the Jiali fault indicate the dominant role of the north advancing eastern Himalayan syntaxis on the surrounding structures. Increased river incision is identified in the middle Salween drainage, leading to two-segment river profiles and further exhumation along the GLSZ and CSSZ. The tributary transient response could result from temporal changes in uplift or adjustments of the trunk channel to climatic change. Furthermore, glaciers play an important role in shaping the landscape of the upper reaches of catchments in the northern segment of the shear zones. Different drivers for the two exhumation events may reflect distinct stages of plateau growth characterized by different crustal deformation patterns

EFCANet: Exposure Fusion Cross-Attention Network for Low-Light Image Enhancement

Image capture devices capture poor-quality images under low-light conditions, and the resulting images have dark areas due to insufficient exposure. Traditional Multiple Exposure Fusion (MEF) methods fuse images with different exposure levels from a global perspective, which often leads to secondary exposure in well-exposed areas of the original image. At the same time, the image sequences with different exposure levels are not sufficient, and the MEF method is limited by the training data and benchmark labels. To address the above problems, this paper proposes an exposure fusion cross-attention network based low-light image enhancement (EFCANet). EFCANet is characterized by recovering normal light images from a single exposure-corrected image. First, the Exposure Image Generator (EIG) is used to estimate the single exposure-corrected image corresponding to the original input image. Then, the color space of the exposure-corrected image and the original input image are converted from RGB to YCbCr, aiming to maintain the balance of brightness and color. Finally, a Cross-Attention Fusion Module (CAFM) is used to fuse the images on the YCbCr color space to achieve image enhancement. We use a single CAFM as a recursive unit, and EFCANet progressively uses four recursive units. The intermediate enhancement results generated by the first recursive unit and the exposure-corrected image of the original input image in YCbCr color space are used as inputs for the second recursive unit. We conducted comparison experiments with 14 state-of-the-art methods on eight publicly available datasets. The experimental results demonstrate that the image quality of EFCANet enhancement is better than other methods

Correlated Mapping Attention Cooperative Network for Urban Remote Sensing Image Segmentation

In current remote sensing segmentation tasks, the difficulty of segmenting spectrally similar objects is a significant issue. Solving this problem is crucial for improving segmentation accuracy. Traditional image-domain segmentation methods rely on color and texture features, but spectrally similar objects have negligible color differences, leading to suboptimal segmentation results. To address this, we propose a network framework called Correlated Mapping Attention Cooperative Network (CMACNet) by extending the problem from the image domain to the feature domain. Image-domain methods depend on color and texture features, whereas feature-domain methods process higher-level abstract features, avoiding issues caused by color similarity. Specifically, CMACNet first employs an autoencoder structure. The autoencoder compresses the input data and attempts to reconstruct the original data, ensuring that the latent space representations capture essential and representative features of the input data, thereby extracting highly generalized and versatile features. Next, we introduce the correlated mapping attention mechanism, which adaptively adjusts the attention to different features based on their correlations, effectively addressing the challenge of segmenting spectrally similar objects. Furthermore, to efficiently establish global relationships among features, we design a cross global interaction layer for global feature remapping. Comprehensive experiments on the Vaihingen and Potsdam datasets demonstrate that CMACNet outperforms existing state-of-the-art methods, achieving mean intersection over union scores of 84.77% and 87.69%, respectively

A Framework and Method for Surface Floating Object Detection Based on 6G Networks

Water environment monitoring has always been an important method of water resource environmental protection. In practical applications, there are problems such as large water bodies, long monitoring periods, and large transmission and processing delays. Aiming at these problems, this paper proposes a framework and method for detecting floating objects on water based on the sixth-generation mobile network (6G). Using satellite remote sensing monitoring combined with ground-truth data, a regression model is established to invert various water parameters. Then, using chlorophyll as the main reference indicator, anomalies are detected, early warnings are given in a timely manner, and unmanned aerial vehicles (UAVs) are notified through 6G to detect targets in abnormal waters. The target detection method in this paper uses MobileNetV3 to replace the VGG16 network in the single-shot multi-box detector (SSD) to reduce the computational cost of the model and adapt to the computing resources of the UAV. The convolutional block attention module (CBAM) is adopted to enhance feature fusion. A small target data enhancement module is used to enhance the network identification capability in the training process, and the key-frame extraction module is applied to simplify the detection process. The network model is deployed in system-on-a-chip (SOC) using edge computing, the processing flow is optimized, and the image preprocessing module is added. Tested in an edge environment, the improved model has a 2.9% increase in detection accuracy and is 55% higher in detection speed compared with SSD. The experimental results show that this method can meet the real-time requirements of video surveillance target detection