Ambiguous Regions Research Articles

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications.

Assessment of the effectiveness of management at the regional level based on the analysis of the activities of authorities

Relevance. The problem of effective management of regional development is becoming more and more urgent due to the emergence of new conditions (obstacles) of functioning, which pose the task of creating programs for the most effective management of subjects to the regions of Russia and executive authorities. It is important to note the choice of the subject: the Kursk region is an important region of the Central Federal District due to its potential and geographical location.The subject has a specialization in important sectors of the entire Russian economy, but at the same time may face inefficient management, which will cause a decline in the regional economy. The purpose is to assess the effectiveness of management of an economic entity on the example of the Kursk region by analyzing the activities of executive authorities.Objectives: to establish the parameters of effective management; to study regional policy as a means of increasing the effectiveness of regional development; to study methods for assessing the effectiveness of regional development through the prism of actions of public authorities; to compare the economic structures of the Kursk region and neighboring regions of the region using specific methodologies; to identify the main problems in the development of the Kursk region.Methodology. To carry out the research, methods of system analysis and synthesis, statistical data processing, as well as comparative and expert analysis are used. The results of the study include an analysis of the effectiveness of regional policy in the Kursk region and the identification of the main problems in the activities of government agencies that affect the economic growth of the subject.Conclusions. The Kursk Region is an ambiguous region regarding the quality of regional economic management. Studying the state support of subjects, their management by authorities, we can say that in the field of ecology and industrial development, the state supports the region sufficiently, since these areas are specialized in the region, which cannot be said about other areas. Despite the weak development of other industries, the state needs to provide significant support to such specialized sectors as agriculture and industry.

Exploring better sparsely annotated shadow detection

Sparsely annotated image segmentation has attracted increasing attention due to its low labeling cost. However, existing weakly-supervised shadow detection methods require complex training procedures, and there is still a significant performance gap compared to fully-supervised methods. This paper summarizes two current challenges in sparsely annotated shadow detection, i.e., weak supervision diffusion and poor structure recovery, and attempts to alleviate them. To this end, we propose a one-stage weakly-supervised learning framework to facilitate sparsely annotated shadow detection. Specifically, we first design a simple yet effective semantic affinity module (SAM) that adaptively propagates scribble supervision to unlabeled regions using a gradient diffusion scheme. Then, to better recover shadow structures, we introduce a feature-guided edge-aware loss, which leverages higher-level semantic relations to perceive shadow boundaries, while avoiding interference from ambiguous regions. Finally, we present an intensity-guided structure consistency loss to ensure that the same images with different brightness are predicted to be consistent shadow masks, which can be regarded as a self-consistent mechanism to improve the model’s generalization ability. Experimental results on three benchmark datasets demonstrate that our approach significantly outperforms previous weakly-supervised methods and achieves competitive performance in comparison to recent state-of-the-art fully-supervised methods.

Antenna resource allocation for netted radar system with main-lobe deceptive jamming suppression

An adaptive beamforming method based on the polarimetric scattering matrix (PSM) of targets to suppress deceptive jamming located in the beam pattern main lobe is proposed in this study. For the netted radar system, the true/false mixed targets of different/same range ambiguity region are distinguished by the range-PSM dimension; the flexible allocation of antenna resource is realised by the number of estimated targets; the main-lobe deceptive jamming is also suppressed by the adaptive beamforming method. First, a polarimetric multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA) was considered as the initial antenna configuration. Subsequently, the performance of the transmit–receive spatial frequency was analysed, and the characteristics of false targets in the polarimetric FDA-MIMO radar were discussed. We then considered the optimisation problem between the transmit polarisation and the frequency increment, as well as the allocation of antenna resources. Finally, an adaptive beamforming method was utilised to achieve deceptive jamming suppression in the main lobe. The effectiveness of PSM was verified with respect to identifying true/false targets, and the jamming suppression performance was evaluated in the main lobe by comparing it with existing systems via simulation results.

Two-photon imaging of rabbit mammary tissue with 3D reconstruction

A broadband two-photon fluorescence (TPF) imaging system, based on a femtosecond oscillator, has been constructed. This system enables TPF imaging of rabbit mammary gland tissue samples by detecting TPF signals generated from the tissue upon excitation by broadband femtosecond pulses. Specifically, TPF imaging utilizes a detection window of 565–615 nm to capture TPF signals from the fluorescent adenine dinucleotide (FAD) present in lactating cells within breast tissue. Moreover, the 3D reconstruction algorithm has been optimized to visualize the 3D structure of the rabbit mammary gland acinar cavity.The optimized three-dimensional reconstruction algorithm can reduce ambiguous signal regions, making the reconstructed three-dimensional structure more reliable and accurate. This is of significant importance for studying the microscopic structure and cell distribution of breast tissue, contributing to a deeper understanding of the tissue's physiological status.

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning

Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer.

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Adaptively identify and refine ill-posed regions for accurate stereo matching

Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such as occluded or ambiguous regions of the images, making it difficult to explore hidden correspondences. To address this challenge, we introduce an Error-area Feature Refinement Mechanism (EFR) that supplies context features for ill-posed regions. In EFR, we innovatively obtain the suspected error region according to aggregation perturbations, then a simple Transformer module is designed to synthesize global context and correspondence relation with the identified error mask. To better overcome existing texture overfitting, we put forward a Dual-constraint Cost Volume (DCV) that integrates supplementary constraints. This effectively improves the robustness and diversity of disparity clues, resulting in enhanced details and structural accuracy. Finally, we propose a highly accurate stereo matching network called Error-rectify Feature Guided Stereo Matching Network (ERCNet), which is based on DCV and EFR. We evaluate our model on several benchmark datasets, achieving state-of-the-art performance and demonstrating excellent generalization across datasets. The code is available at https://github.com/dean7liu/ERCNet_2023.

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions. Using recent advancements in generative models, we derive uncertainty intervals around principal components of the empirical posterior distribution, forming an ambiguity region that guarantees the inclusion of true unseen values with a user-defined confidence probability. To improve computational efficiency and interpretability, we also guarantee the recovery of true unseen values using only a few principal directions, resulting in more informative uncertainty regions. Our approach is verified through experiments on image colorization, super-resolution, and inpainting; its effectiveness is shown through comparison to baseline methods, demonstrating significantly tighter uncertainty regions.

Automated extraction of Tridacna shell growth patterns via machine learning for enhanced paleoclimate/paleoweather research

Tridacna spp. are valuable archives for paleoclimate and paleoweather research due to their distinct daily growth patterns and the sensitivity of the daily growth patterns to environment changes. However, manually identifying daily growth lines and measuring the daily growth increment width (DGIW) of Tridacna shells from Laser Scanning Confocal Microscopy (LSCM) images is a tedious task that has become a significant barrier to Tridacna studies. This paper addresses this challenge by integrating machine learning into Tridacna research for the first time to automate the calculation of the number of daily growth lines and DGIW of Tridacna shells. Specifically, we propose an unsupervised generative adversarial attention network called TriGAN to automatically recognize distinct daily growth lines of Tridacna shells from LSCM images. Utilizing modern Tridacna specimens collected from the South China Sea, our experimental results demonstrate that TriGAN can effectively reconstruct the ambiguous and blurred regions in LSCM images and produce higher quality images of daily growth patterns compared to existing image generation networks. Furthermore, the daily growth line number and DGIW of Tridacna shells can be counted automatically from the images recognized by TriGAN, which are in good agreement with the statistical results obtained manually from the original LSCM images (R = 0.7, p < 0.01 for the DGIW profile of T. gigas specimen MD1 and R = 0.6, p < 0.01 for T. derasa specimen XB10). This automated method provides an efficient solution for researching the laminar chronology of Tridacna.

View synthesis with multiplane images from computationally generated RGB-D light fields

Image based view synthesis using deep neural networks provide novel scene views from a set of captured single or multiple images. Multiplane images (MPI) represent scene content as set of RGBα planes within a reference view frustum and render novel views by projecting the content into the target viewpoints. Image based view synthesis with multiple images is very popularly deployed in various areas because it effectively represents geometric uncertainty in ambiguous regions and can convincingly simulate non-Lambertian effects. However, previous image based view synthesis approaches suffer from interpolating and extrapolating information in pixels or ray spaces to generate seamless novel views without occlusion. To effectively improve visual performance for view interpolation and extrapolation, this paper proposes a novel view synthesis with MPI images. From a monocular RGB image, light field images are computationally generated, the proposed depth map guided deep network produces robust MPI using the light field images and their corresponding depth images, and the MPI network embedded with depth attention blocks forces semantic and geometric information to be uniformly distributed and divided among layers. The proposed approach achieves 3.5% and 4.02% improvements in SSIM and PSNR values, comparing to the SOTA approaches. Qualitative analysis on benchmark dataset also verifies the robustness of the proposed approach.

Hierarchical Graph Interaction Transformer With Dynamic Token Clustering for Camouflaged Object Detection.

Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.

BCUIS‐Net: A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion

AbstractBreast cancer is a highly lethal disease with the highest mortality rate among women worldwide. Breast tumor segmentation from ultrasound images plays a critical role in enabling early detection, leading to a reduction in mortality rates. However, the challenge of ultrasound breast cancer segmentation arises from factors such as indistinct lesion boundaries, noise artifacts, and inhomogeneous intensity distribution within the lesion region. To address the bottlenecks, a novel boundary‐aware shape feature fusion network (BCUIS‐Net) is proposed to segment breast lesion in ultrasound images. Firstly, a boundary‐aware module (BAM) is put forward to accurately localize the ambiguous tumor regions and boundaries by embedding the horizontal and vertical position information into the channel attention. Subsequently, a shape feature fusion (SFF) module is presented to fuse shape features and segmentation features, in order to adaptively extract their complementary features by aggregating contextual information in an attention module. Specifically, the different levels of features from the encoder are up‐sampled to the original image size and fed into the BAM to predict the boundary map. The boundary and decoder‐generated feature maps are thereafter fused by the SFF module to exploit the complementarity between them to correct errors in segmentation and shape features, effectively eliminating false detections and noise in the features to achieve accurate segmentation of pathological regions. Finally, the shape fusion loss is derived from a combination of the binary cross‐entropy loss and the distance map loss to intelligently penalize incorrect predictions and thus improve the attention to boundary locations. The performance of the network is evaluated in two public breast ultrasound datasets. Experimental results verify that the proposed method obtains superior segmentation results and outperforms the most recent state‐of‐the‐art, in which IOU is increased by 2.15% and 2.59% on UDIAT and BUSI, respectively.

Survival classification of Gliomas through a novel enhancement-based strategy for class overlap of radiomics features

Conventional radiomics-based models precisely engineered image-based features from Magnetic Resonance Imaging (MRI) to extract the predictive patterns for High-Grade Gliomas (HGGs) survival prediction. But an in-depth exploration and assessment of these extracted features are still not conducted. To the best of our knowledge, this is the first study that perceives the range distributions of radiomics features and gains insight into the problem of class overlap among these features. A novel class-wise feature enhancement strategy addresses the ambiguous data regions in the extracted features without any data loss. This strategy explicitly tunes the data values of only two classes and retains the data of the third class to achieve excellent data separability. The enhancement depends on the difference in the feature values of the two classes and incorporates the scalability of this difference using different scaling factors. Furthermore, Box-Cox and logarithmic transformations are employed to overcome the non-normality of the enhanced features. Consequently, ablation experimentation is conducted to substantiate the classification metrics with pre- and post-enhancement cases. BraTS 2020 benchmark is employed, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors. It achieves an overall testing classification accuracy, precision, recall, and F1-score of 0.994, 0.993, 0.996, and 0.994, respectively. Therefore, instead of directly utilizing these raw extracted features, this strategy eliminates the overlapped class regions without any information loss and has proven to be a significant step for HGG survival classification applications.

A Method to Suppress Interferences Based on Secondary Compensation with QPC-FDA-MIMO Radar

The traditional phased array radar is unable to distinguish mainlobe deceptive interference from the true target when they share an identical angle. To address this issue, a method is developed to suppress the interference in the joint transmit–receive–Doppler frequency domain, using secondary compensation and quadratic phase code (QPC) with the frequency diversity array multiple-input multiple-output (FDA-MIMO) radar. The QPC technique is employed by encoding the transmitted pulses in various spatial channels, which extends the degrees-of-freedom (DOFs) of the system. To distinguish and suppress the false targets behind the true target by at least one pulse, Doppler compensation is performed and Doppler filters are employed. To distinguish the fast-generated false targets, which are located in the same pulse with the true target, the spatial frequency compensation is implemented, which equivalently relocates the mainlobe interference to the sidelobes. Via the singular spectrum analysis (SSA) technique and the data-dependent beamforming, the interferences within the ambiguity region are suppressed. Simulation results verify the effectiveness of the method.

First Report of Banana mild mosaic virus Infecting Banana in China.

Banana (Musa spp.) is an important fruit in tropical and subtropical regions and an essential food crop in some developing countries. China has a long history of banana cultivation and ranks second in global banana production, with a planting area exceeding 11 million hectares (FAOSTAT, 2023). Banana mild mosaic virus (BanMMV) is a flexuous filamentous virus infecting bananas and a banmivirus in the Betaflexiviridae family. Its infection often results in symptomless plants of Musa spp., and the virus has a worldwide distribution, which can explain its high prevalence (Kumar et al., 2015). BanMMV infection often causes transitory symptoms, such as mild chlorotic streaks and mosaics, on young leaves (Thomas, 2015). The mixed infection of BanMMV with other banana-infecting viruses such as banana streak viruses (BSV) and cucumber mosaic virus (CMV), can exacerbate the mosaic symptoms of BanMMV (Fidan et al., 2019). In October 2021, we collected twenty-six leaf samples of suspected viral disease of bananas from four cities (Huizhou, Qingyuan, Zhanjiang, and Yangjiang) in Guangdong province, two cities (Hekou and Jinghong) in Yunnan province, two cities (Yulin and Wuming) in Guangxi Zhuang autonomous region. After fully mixing these infected samples, we divided them into two pools and sent them to Shanghai Biotechnology Corporation (China) for metatranscriptome sequencing. Each sample contained about 5 g of leaves in total. Zymo-Seq RiboFree Total RNA Library Prep Kit (Zymo Research, USA) was used for ribosomal RNA depletion and library preparations. Illumina sequencing (Illumina NovaSeq 6000) was carried out by Shanghai Biotechnology Corporation (China). Paired-end (150 bp) sequencing of the RNA library was performed on an Illumina HiSeq 2000/2500 platform. Clean reads were assembled by a metagenomic de novo assembly using the CLC Genomics Workbench (version: 6.0.4). Then the non-redundant protein database in the National Center for Biotechnology Information (NCBI) was used for BLASTx annotation. A total of 79,528 contigs were generated from the clean reads (68,878,162) through de novo assembly. A contig of 7265 nucleotides (nts) showed the highest nucleotide sequence identity (90.08%) to the genome of BanMMV isolate EM4-2 (GenBank accession no. OL826745.1). We designed specific primers according to the BanMMV CP gene (Table S1), tested the twenty-six leaf samples collected from the above-mentioned eight cities, and found that only one sample of Fenjiao (Musa ABB Pisang Awak) in Guangzhou city was infected with this virus. The symptoms of banana leaves containing BanMMV were slight chlorosis and yellowing of leaf edges (Fig. S1). We failed to detect other banana viruses, such as BSV, CMV, and banana bunchy top virus (BBTV) in the BanMMV-infected banana leaves. RNA from the infected leaves was extracted, and the assembled contig was confirmed by overlapping PCR amplification across the whole sequence (Table S1). All ambiguous regions were amplified by PCR and RACE, and the products were subjected to Sanger sequencing. The complete genome of the virus candidate was 7310 nts in length, excluding the poly (A) tail. The sequence was deposited in GenBank under accession number ON227268 (isolate BanMMV-GZ from Guangzhou). A schematic representation of the genome organization of BanMMV-GZ is shown in Fig. S2. Its genome has five open reading frames (ORF) encoding RNA-dependent RNA polymerase (RdRp), three triple gene block proteins necessary for cell-to-cell movement (TGBp1 to TGBp3) and a coat protein (CP), similar to other BanMMV isolates (Kondo et al., 2021). Phylogenetic analyses of the complete nt sequence of the full genome and RdRp gene using the neighbor-joining (NJ) method also clearly placed the BanMMV-GZ firmly within all isolates of BanMMV (Fig. S3). To our knowledge, this is the first report of BanMMV infecting bananas in China, extending the geographical range of this viral disease around the world. Accordingly, larger-scale BanMMV investigations must be conducted to determine the distribution and prevalence of BanMMV in China.

Learning how to robustly estimate camera pose in endoscopic videos

PurposeSurgical scene understanding plays a critical role in the technology stack of tomorrow’s intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs.MethodWe propose a solution for stereo endoscopes that estimates depth and optical flow to minimize two geometric losses for camera pose estimation. Most importantly, we introduce two learned adaptive per-pixel weight mappings that balance contributions according to the input image content. To do so, we train a Deep Declarative Network to take advantage of the expressiveness of deep learning and the robustness of a novel geometric-based optimization approach. We validate our approach on the publicly available SCARED dataset and introduce a new in vivo dataset, StereoMIS, which includes a wider spectrum of typically observed surgical settings.ResultsOur method outperforms state-of-the-art methods on average and more importantly, in difficult scenarios where tissue deformations and breathing motion are visible. We observed that our proposed weight mappings attenuate the contribution of pixels on ambiguous regions of the images, such as deforming tissues.ConclusionWe demonstrate the effectiveness of our solution to robustly estimate the camera pose in challenging endoscopic surgical scenes. Our contributions can be used to improve related tasks like simultaneous localization and mapping (SLAM) or 3D reconstruction, therefore advancing surgical scene understanding in minimally invasive surgery.

Wasserstein distance‐based distributionally robust parallel‐machine scheduling

Recent research on distributionally robust (DR) machine scheduling has used a variety of approaches to describe the region of ambiguity of uncertain processing times by imposing constraints on the moments of the probability distributions. One approach that has been employed outside machine scheduling research is the use of statistical metrics to define a distance function between two probability distributions. Adopting such an approach, we study Wasserstein distance-based DR parallel-machine scheduling, where the ambiguity set is defined as a Wasserstein ball around an empirical distribution of uncertain processing times corresponding to finitely many samples. The objective is to minimize a DR objective that concerns the worst-case expected total completion time-related cost over all the distributions arising from the Wasserstein ambiguity set, subject to DR chance constraints on the machine service capacity. We show that the problem can be equivalently re-formulated as a mixed-integer linear program (MILP), which has a more simplified formulation when the bounded support set reduces to a left bounded one. To solve the resulting model, we develop a tailored branch-and-Benders-cut algorithm incorporating some enhancement strategies, including in-out Benders cut generation, aggregated sample group cut generation, and two-stage Benders cut generation, which significantly outperforms the CPLEX solver. Experiment results on comparing our model with the deterministic and stochastic counterparts and the model with first-order moment ambiguity set illustrate the benefits of considering distributional ambiguity and Wasserstein ambiguity set.

An Attention-Based Uncertainty Revising Network with Multi-Loss for Environmental Microorganism Segmentation

The presence of environmental microorganisms is inevitable in our surroundings, and segmentation is essential for researchers to identify, understand, and utilize the microorganisms; make use of their benefits; and prevent harm. However, the segmentation of environmental microorganisms is challenging because their vague margins are almost transparent compared with those of the environment. In this study, we propose a network with an uncertainty feedback module to find ambiguous boundaries and regions and an attention module to localize the major region of the microorganism. Furthermore, we apply a mid-pred module to output low-resolution segmentation results directly from decoder blocks at each level. This module can help the encoder and decoder capture details from different scales. Finally, we use multi-loss to guide the training. Rigorous experimental evaluations on the benchmark dataset demonstrate that our method achieves higher scores than other sophisticated network models (95.63% accuracy, 89.90% Dice, 81.65% Jaccard, 94.68% recall, 0.59 ASD, 2.24 HD95, and 85.58% precision) and outperforms them.

Cooperated Moving Target Detection Approach for PA-FDA Dual-Mode Radar in Range-Ambiguous Clutter

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios.