Partial Occlusion Research Articles

DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth-Based 3D Reconstruction

Although deep networks-based 3D reconstruction methods can recover the 3D geometry given few inputs, they may produce unfaithful reconstruction when predicting occluded parts of 3D objects. To address the issue, we propose Detail-Enhanced Generative Adversarial Network (DEGAN) which consists of Encoder–Decoder-Based Generator (EDGen) and Voxel-Point Embedding Network-Based Discriminator (VPDis) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGen decodes the features from the 2.5D voxel grid representation of an input depth image and generates the 3D occupancy grid under GAN losses and a sampling point loss. The sampling loss can improve the accuracy of predicted points with high uncertainty. VPDis helps reconstruct the details under voxel and point adversarial losses, respectively. Experimental results show that DEGAN not only outperforms several state-of-the-art methods on both public ModelNet and ShapeNet datasets but also predicts more reliable occluded/missing parts of 3D objects.

An Enhanced Feature Learning CNN Using Auto-encoder for Partially Occluded Target Recognition in SAR Imagery

Deep learning algorithms have found extensive utility in Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR). Despite this, conventional deep learning-based ATR models often lack in their performance when subjected to Extended Operating Conditions (EOCs). Significant variations in depression, noise interference, limited training data, and partial occlusion are primary factors in EOCs. In this paper, we proposed a deep neural network-based technique to recognize SAR targets for Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed approach comprises an encoder and decoder network. The encoder network integrates multiple dilated convolutions to address noise-induced recognition challenges for extracting multi-scale features. Furthermore, a feature refinement module is integrated within the multi-scale channels to enhance the extraction of discriminative and resilient features. Feature refinement selectively emphasizes informative features while suppressing irrelevant ones. Additionally, a salient attention block and a spatial feature learning module are introduced for feature selection. Selected features are then utilized in the top-level network layer of the encoder network to preserve spatial relationships among diverse features. This spatial feature learning module aids in recognition performance, particularly when training data is scarce. The decoder network consists of stacked transposed convolution layers, facilitating the encoder network's acquisition of discriminative image features. Experimental evaluations were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to validate the efficacy of the proposed methodology.

Three-Dimensional Instance Segmentation Using the Generalized Hough Transform and the Adaptive n-Shifted Shuffle Attention

The progress of 3D instance segmentation techniques has made it essential for several applications, such as augmented reality, autonomous driving, and robotics. Traditional methods usually have challenges with complex indoor scenes made of multiple objects with different occlusions and orientations. In this work, the authors present an innovative model that integrates a new adaptive n-shifted shuffle (ANSS) attention mechanism with the Generalized Hough Transform (GHT) for robust 3D instance segmentation of indoor scenes. The proposed technique leverages the n-shifted sigmoid activation function, which improves the adaptive shuffle attention mechanism, permitting the network to dynamically focus on relevant features across various regions. A learnable shuffling pattern is produced through the proposed ANSS attention mechanism to spatially rearrange the relevant features, thus augmenting the model’s ability to capture the object boundaries and their fine-grained details. The integration of GHT furnishes a vigorous framework to localize and detect objects in the 3D space, even when heavy noise and partial occlusions are present. The authors evaluate the proposed method on the challenging Stanford 3D Indoor Spaces Dataset (S3DIS), where it establishes its superiority over existing methods. The proposed approach achieves state-of-the-art performance in both mean Intersection over Union (IoU) and overall accuracy, showcasing its potential for practical deployment in real-world scenarios. These results illustrate that the integration of the ANSS and the GHT yields a robust solution for 3D instance segmentation tasks.

Polarimetric 3D integral imaging profilometry under degraded environmental conditions

We propose polarimetric three-dimensional (3D) integral imaging profilometry and investigate its performance under degraded environmental conditions in terms of the accuracy of object depth acquisition. Integral imaging based profilometry provides depth information by capturing and utilizing multiple perspectives of the observed object. However, the performance of depth map generation may degrade due to light condition, partial occlusions, and object surface material. To improve the accuracy of depth estimation in these conditions, we propose to use polarimetric profilometry. Our experiments indicate that the proposed approach may result in more accurate depth estimation under degraded environmental conditions. We measure a number of metrics to evaluate the performance of the proposed polarimetric profilometry methods for generating the depth map under degraded conditions. Experimental results are presented to evaluate the robustness of the proposed method under degraded environment conditions and compare its performance with conventional integral imaging. To the best of our knowledge, this is the first report on polarimetric 3D integral imaging profilometry, and its performance under degraded environments.

Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Sharing Network (MAADS) is proposed in this paper. MAADS comprises a Feature Discrimination Network (FDN), an Attention Distraction Network (ADN), and a Shared Fusion Network (SFN). To be specific, FDN first integrates attention weights into the objective function to capture the most discriminative features by using the proposed sparse affinity loss. Then, ADN employs multiple parallel attention networks to maximize diversity within spatial attention units and channel attention units, which guides the network to focus on distinct, non-overlapping facial regions. Finally, SFN deconstructs facial features into generic parts and unique parts, which allows the network to learn the distinctions between these features without having to relearn complete features from scratch. To validate the effectiveness of the proposed method, extensive experiments were conducted on several widely used in-the-wild datasets including RAF-DB, AffectNet-7, AffectNet-8, FERPlus, and SFEW. MAADS achieves the accuracy of 92.93%, 67.14%, 64.55%, 91.58%, and 62.41% on these datasets, respectively. The experimental results indicate that MAADS not only outperforms current state-of-the-art methods in recognition accuracy but also has a relatively low computational complexity.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Diagnosing heart failure with preserved ejection fraction in patients with unexplained dyspnoea using left ventricular pressure-volume loops at rest and on exertion

Abstract Background To date, diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, especially in euvolaemic patients presenting with unexplained dyspnoea on exertion and reduced exercise capacity. A stepwise approach was introduced with the HFA-PEFF diagnostic algorithm*, in which a relevant number of patients has to undergo invasive workup for a final judgement. Purpose The present study aimed to examine a population of patients with unexplained dyspnoea on exertion, using the diagnostic methods endorsed by the HFA-PEFF algorithm as well as left ventricular (LV) pressure-volume loops at rest, on exertion, and during caval occlusion, in order to identify and describe different phenotypes of HFpEF. Methods We prospectively included patients with unexplained dyspnoea on exertion, a left ventricular ejection fraction (LVEF) of ≥ 50%, and without an obvious cause of the dyspnoea. All patients underwent a comprehensive invasive haemodynamic workup. After invasive rule-out of a haemodynamically relevant coronary artery stenosis, we performed a right heart catheterization at rest (R) and on exertion (E) with an arm ergometer. Real-time continuous pressure-volume loops were obtained using a 7-French conductance catheter at rest, on exertion, and under temporal preload reduction via partial occlusion of the vena cava inferior. Results Thirty patients were included. Two patients had to be excluded from further analysis (one due to a technical failure of the conductance catheter and one with severe hypertension (systolic blood pressure > 200 mmHg) during the assessment). The 28 patients in this analysis had a mean age of 64.4±9.7 years and 57% were female. Eleven patients fulfilled invasively easily obtainable diagnostic criteria of HFpEF (mean pulmonary capillary wedge pressure (mPCWP) at rest ≥ 15 mmHg, exertional mPCWP ≥ 25 mmHg, LV end-diastolic pressure (LVEDP) at rest ≥ 16 mmHg, or exertional LVEDP ≥ 25 mmHg) according to the current guideline*. An additional 15 patients had a normal mPCWP and LVEDP, but fulfilled diagnostic criteria for HFpEF measured by the conductance catheter (time constant of LV relaxation tau > 48 ms and/ or chamber stiffness b > 0.27). We referred to this group as "early HFpEF". Patients with early HFpEF had a significantly higher systolic blood pressure, a worse LV relaxation time, and were more likely to exhibit chronotropic insufficiency compared to the HFpEF group (Table 1). HFpEF patients were characterised by significantly higher natriuretic peptides, LVEDP on exertion, and LV stiffness compared to patients with early HFpEF (pressure-volume loops are depicted in Figure 1). Conclusion Invasive haemodynamic testing for HFpEF in patients with unexplained dyspnoea on exertion via right heart catheterization without obtaining LV pressure-volume loops had a false negative rate of 58%. Better detection, the prognosis, and treatment of this "early HFpEF" population warrants further research.Table 1Figure 1

YOLOv8n-CSD: A Lightweight Detection Method for Nectarines in Complex Environments

At present, the picking of nectarines mainly relies on manual completion in China, and the process involves high labor intensity during picking and low picking efficiency. Therefore, it is necessary to introduce automated picking. To improve the accuracy of nectarine fruit recognition in complex environments and to increase the efficiency of automatic orchard-picking robots, a lightweight nectarine detection method, YOLOv8n-CSD, is proposed in this study. This model improves on YOLOv8n by first proposing a new structure, C2f-PC, to replace the C2f structure used in the original network, thus reducing the number of model parameters. Second, the SEAM is introduced to improve the model’s recognition of the occluded part. Finally, to realize real-time detection of nectarine fruits, the DySample Lightweight Dynamic Upsampling Module is introduced to save computational resources while effectively enhancing the model’s anti-interference ability. With a compact size of 4.7 MB, this model achieves 95.1% precision, 84.9% recall, and a mAP@0.5 of 93.2%—the model’s volume has been reduced while the evaluation metrics have all been improved over the baseline model. The study shows that the YOLOv8n-CSD model outperforms the current mainstream target detection models, and can recognize nectarines in different environments faster and more accurately, which lays the foundation for the field application of automatic picking technology.

Partial Occlusion, Less AKI: an AORTA Registry Analysis of pREBOA-PRO

Abstract Background Non-compressible torso hemorrhage remains a leading cause of potentially preventable deaths. Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) has emerged as an adjunct temporizing hemorrhage control. The complete occlusion strategy with the ER-REBOA catheter can cause distal ischemia when used for longer than 30 minutes. To specifically address this limitation, the pREBOA-PRO catheter was developed. The objective of the current study is to investigate the impact of longer, partial occlusion times provided by pREBOA-PRO on acute kidney injury and mortality. Methods This is a retrospective analysis of the Aortic Occlusion for Resuscitation in Trauma and Acute Care Surgery (AORTA) registry as of August 2023. Patients in the pREBOA-PRO group (n = 110) receiving partial occlusion in Zone 1 were contrasted to patients in the ER-REBOA group (n = 752) treated with complete occlusion in Zone 1. Between-group comparisons included demographics, clinical presentation, aortic occlusion (AO) strategy, clinical complications, and mortality. Results Demographics, clinical presentation, and mortality were indistinguishable between groups, except for age which was higher in the ER-REBOA group (44 vs. 38, p < 0.002). The partial AO strategy was employed more often in the pREBOA-PRO group (87% vs 33%, p < 0.05) and for longer periods (59 min vs. 50 min, p < 0.003). In contrast, AKI occurred less frequently in the pREBOA-PRO group (19% vs. 33%, p < 0.05). Conclusions The more frequently partial and longer occlusion times in Zone 1 with the use of pREBOA-PRO resulted in lower AKI incidence suggesting that this newer device is a safer extended bridge to hemorrhage control.

A mattress suture with pledgets can partially occlude intrahepatic shunts in 23 dogs: evaluation of long-term survival.

To explore the differences in outcomes between dogs with intrahepatic portosystemic shunts (IHPSSs) partially occluded with a mattress suture with pledgets or attenuated with cellophane banding. 74 dogs with IHPSSs were included in this retrospective study. Medical records between January 2000 and February 2020 were evaluated for information on dogs undergoing laparotomy with cellophane banding (group C) or mattress suture with pledgets (group P) for treatment of IHPSSs, including specific outcomes of survival time, persistence of clinical signs, and necessity of continued medical management. Primary veterinarians and owners were contacted for long-term follow-up. 51 dogs were entered in group C and 23 in group P. The duration of surgery was 154.6 minutes (136.3 to 170 minutes) for group C and 154.9 minutes (128.9 to 180.9 minutes) for group P. The short-term mortality rate was 2% for group C and 4% for group P. Dogs in group C had a median survival time of 3,537 days, while dogs in group P had a median survival time of 2,141 days. In the long term, dogs in group P received more medications than dogs in group C, but the distribution of the quality-of-life score was not different. Mattress suture with pledgets can be used as an alternative to cellophane banding. For dogs with IHPSSs where full surgical dissection for placement of cellophane band is not possible, partial occlusion of the shunt with a mattress suture with pledgets is a viable option with acceptable long-term outcomes.

Implied occlusion and subset underestimation contribute to the weak-outnumber-strong numerosity illusion.

Four experimental studies are reported using a total of 712 participants to investigate the basis of a recently reported numerosity illusion called "weak-outnumber-strong" (WOS). In the weak-outnumber-strong illusion, when equal numbers of white and gray dots (e.g., 50 of each) are intermixed against a darker gray background, the gray dots seem much more numerous than the white. Two principles seem to be supported by these new results: 1) Subsets of mixtures are generally underestimated; thus, in mixtures of red and green dots, both sets are underestimated (using a matching task) just as the white dots are in the weak-outnumber-strong illusion, but 2) the gray dots seem to be filled in as if partially occluded by the brighter white dots. This second principle is supported by manipulations of depth perception both by pictorial cues (partial occlusion) and by binocular cues (stereopsis), such that the illusion is abolished when the gray dots are depicted as closer than the white dots, but remains strong when they are depicted as lying behind the white dots. Finally, an online investigation of a prior false-floor hypothesis concerning the effect suggests that manipulations of relative contrast may affect the segmentation process, which produces the visual bias known as subset underestimation.

Radar gait recognition using Dual-branch Swin Transformer with Asymmetric Attention Fusion

Video-based gait recognition suffers from potential privacy issues and performance degradation due to dim environments, partial occlusions, or camera view changes. Radar has recently become increasingly popular and overcome various challenges presented by vision sensors. To capture tiny differences in radar gait signatures of different people, a dual-branch Swin Transformer is proposed, where one branch captures the time variations of the radar micro-Doppler signature and the other captures the repetitive frequency patterns in the spectrogram. Unlike natural images where objects can be translated, rotated, or scaled, the spatial coordinates of spectrograms and CVDs have unique physical meanings, and there is no affine transformation for radar targets in these synthetic images. The patch splitting mechanism in Vision Transformer makes it ideal to extract discriminant information from patches, and learn the attentive information across patches, as each patch carries some unique physical properties of radar targets. Swin Transformer consists of a set of cascaded Swin blocks to extract semantic features from shallow to deep representations, further improving the classification performance. Lastly, to highlight the branch with larger discriminant power, an Asymmetric Attention Fusion is proposed to optimally fuse the discriminant features from the two branches. To enrich the research on radar gait recognition, a large-scale NTU-RGR dataset is constructed, containing 45,768 radar frames of 98 subjects. The proposed method is evaluated on the NTU-RGR dataset and the MMRGait-1.0 database. It consistently and significantly outperforms all the compared methods on both datasets. The codes are available at:https://github.com/wentaoheunnc/NTU-RGR.

DEEP Q-NETWORK (DQN) PARTIALOCCLUSION SEGMENTATION AND BACKTRACKING SEARCH OPTIMIZATION ALGORITHM (BSOA) WITH OPTICAL FLOW RECONSTRUCTION FOR FACIAL EXPRESSION EMOTION RECOGNITION

Video facial expression recognition (FER) has garnered a lot of attention recently and is helpful for several applications. Although many algorithms demonstrate impressive performance in a controlled environment without occlusion, identification in the presence of partial facial occlusion remains a challenging issue. Solutions based on reconstructing the obscured area of the face have been suggested as a way to deal with occlusions. These options mostly rely on the face’s shape or texture. Nonetheless, the resemblance in facial expressions among individuals appears to be a valuable advantage for the reconstruction. For semantic segmentation based on occlusions, Reinforcement Learning (RL) is introduced as the initial stage. From a pool of unlabeled data, an agent learns a policy to choose a subset of tiny informative image patches to be tagged instead of full images. In the second stage, a trained Backtracking Search Algorithm (BSA) is used to rebuild optical flows that have been distorted by the occlusion. On obtaining optical flows estimated from occluded facial frames, AEs restore optical flows of occluded regions. These recovered optical flows become inputs to anticipate classes f expressions. Optical flux reconstructions then classify stages. This study evaluates classification model’s performances for face expression identification based on Very Deep Convolution Networks (VGGNet). Furthermore, it produces more accurate confusion matrices and proposes approaches for the KMU-FED and CK+ databases, respectively. The results are evaluated using metrics including recall, f-measure, accuracy, and precision.

Robust Face Recognition System Based on Deep Facial Feature Extraction and Machine Learning

Facial Recognition is a common challenge in research for security applications. Wide-ranging applications of this technology include biometrics, security data, access to controlled locations, smart cards, and surveillance systems. A practical approach to identifying individuals depends on factors such as partial face occlusion, illumination, age, expression, makeup, and poses. These complex face recognition variables affect most face recognition systems. In this paper, we propose a deep transfer learning for facial features based on the pose and illumination variations of celebrities’ faces in a free environment for face recognition. The new approach that combines deep learning methods for feature extraction with machine learning classification methods. Also, studies the performance of the pre-trained model (VGGFace2 with ResNet-50 model weights) for feature extraction with a Multilayer Perceptron Classifier (MLP), Decision Tree, and Bootstrap Aggregation (Bagging) to perform classification. The convolution neural network has lately achieved excellent advancement in facial recognition. The outcome indicated that VGGFace2 with MLP obtained more precision and provided the best results (Accuracy, F-measure, Recall, and Precision). The results for all metric for the proposed model is 99%. The benchmark for this model is a dataset of 105 people’s faces.

Long-term liver deportalisation in stable and time prolonged prehepatic portal hypertension model affects expression of hypoxia, angiogenesis, apoptosis, and autophagy markers in the young rats’ liver

prehepatic portal hypertension in children cause severe and life-threatening complications, that highlights the need in in-depth investigations of pathogenetic mechanisms, which contribute to prehepatic portal hypertension-associated liver pathology. In developed stable experimental prehepatic portal hypertension model in adolescent rat males we aimed to assess the expression levels of the key molecular markers of hypoxia, angiogenesis, autophagy, and apoptosis in the liver tissue after 6-month deportalization. Partial portal vein ligation was performed in 4-week old male rats. After 6-months tissue samples from PHP-model, sham operated and control animals were studied by western blot to identify protein levels of markers related to prehepatic portal hypertension -induced liver injury. Partial portal vein occlusion upregulated hypoxia inducible factor -1α (by 4.67 folds vs. control, p<0.001) and vascular endothelial growth factor protein expression levels (by 2.33 folds vs. control, p<0.001) suggesting chronic hypoxia development. Abnormally high levels of angiostatin isoforms were found (by 9.88 folds vs. control, p<0.001) to signify angiogenesis dysregulation. Significant caspase-3 (23.4-fold increase vs. control, p<0.001) overexpression is executive phase of apoptotic cell death evidence. Dramatic LC3 level expression indicates existence of crosstalk between autophagy and apoptosis that contribute to fibrotic changes. Hypoxia-induced events, impaired angiogenesis regulation, enhanced autophagy and apoptosis are contributing factors of prehepatic portal hypertension -induced liver injury. A better understanding of subtle molecular mechanisms of this pathology may pave the way for innovative treatment options.

Evaluation of Novel Biomarker of Early Atherosclerosis in Patients With Chronic Kidney Disease in Wasit Governorate

The aim of the study was to evaluate some Biomarkers in patients with athreoscelerosis in Wasit Province. Atherosclerosis is a chronic arterial disease and a major cause of vascular death. Fatty streaks in arterial walls gradually develop into atheroma and characteristic plaques. The acute rupture of these atheromatous plaques causes local thrombosis, leading to partial or total occlusion of the affected artery. The clinical consequences of these plaques depend on their site and the degree and speed of vessel occlusion . Its major clinical manifestations include ischemic heart disease (IHD), ischemic stroke, and peripheral arterial disease. The study took place in the a hemodialysis unit Al-Zahraa Teaching Hospital in Wasit Governorate during the period from November 2023 to April 2024. This study aimed to examine risk factors (age, gender, high blood pressure, smoking, left ventricular hypertrophy, diabetes mellitus, dyslipidemia, physical inactivity) for atherosclerosis in the general population and patients with chronic kidney disease. Also studying the effect of some novel biomarkers (Homocysteine, Fibrinogen, myeloperoxidase, lipoprotein-associated phospholipase A2, pentraxin 3). The study include (90) samples: (60) patients suffering from early atherosclerosis and chronic kidney disease (dialysis stage) and 30 samples of healthy people (control group), their ages ranged from (40 to 70 years). The samples were divided into two age groups, where the first group had an average age of 40-55 years and the second group was 56-70 years old , the categories were divided equally into males and females Samples were collected based on the incidence of kidney disease first, and then the main risk factors of early atherosclerosis were relied upon, which are (smoking, age, sex, dyslipidemia, hypertension, diabetes, left ventricle valve, physical inactivity). The results of our study regarding diagnostic markers of atherosclerosis showed a significant increase in biomarkers (MPO, PTX3, HCy, FG and LpPLA2)of infected people at p ≥ 0.5. In patients with atherosclerosis the level of new biomarkers showed a significant increase in serum of infected people compared to healthy people at p<0.5. This is an important indication of the importance of these biomarkers in detecting the disease.

A parametric approach for creating finite element models from point clouds for steel superstructure components in steel girder bridges

Finite element (FE) analysis is widely used to evaluate the conditions of existing bridges. However, the creation of high-fidelity FE models (FEM) for existing bridges is often a challenging task. In view of the ability of 3D laser scanning to capture detailed geometrical information, this study proposes a direct scan-to-FEM strategy specifically for steel bridge superstructure and hence to facilitate the creation of high-fidelity FE models for existing steel girder bridges. Parametric representations are proposed to address partial occlusions in the point clouds as well as to reinforce the connectivity and alignment between the superstructure elements. Mesh generation algorithms are developed to automatically convert the parametric representations into all-hexahedron FE mesh assembly that can be used directly for FE analysis without any mesh verification or editing. Experimental validations are carried out to evaluate the effectiveness of proposed method in term of both mesh quality and analysis accuracy.

Face Recognition in the Modern Era: A Survey of Techniques, Challenges, and Innovations

This review paper talks about the development of face recognition, ranging from traditional methods to the latest methods of deep learning (Hasan et al., 2021; Sáez-Trigueros et al., 2018). The early methods were based on separate characteristics like SIFT and LBP (Balaban, 2015) that could not handle complex scenarios. The use of statistical subspace methods helped to improve face representation. Deep learning, however, has transformed the domain, where systems like DeepFace attain nearly human-like performance by taking advantage of vast and various datasets (Taigman et al., 2014). Nonetheless, the bias, fairness, and privacy issues that remain unsolved have led to the ongoing research to make the face recognition systems more robust and ethically acceptable. Keywords--- Face recognition; Illuminations; partial occlusion; pose invariance

Bistable perception of symbolic numbers.

Numerals, that is, semantic expressions of numbers, enable us to have an exact representation of the amount of things. Visual processing of numerals plays an indispensable role in the recognition and interpretation of numbers. Here, we investigate how visual information from numerals is processed to achieve semantic understanding. We first found that partial occlusion of some digital numerals introduces bistable interpretations. Next, by using the visual adaptation method, we investigated the origin of this bistability in human participants. We showed that adaptation to digital and normal Arabic numerals, as well as homologous shapes, but not Chinese numerals, biases the interpretation of a partially occluded digital numeral. We suggest that this bistable interpretation is driven by intermediate shape processing stages of vision, that is, by features more complex than local visual orientations, but more basic than the abstract concepts of numerals.

Management of Myocardial Infarction and the Role of Cardiothoracic Surgery.

Myocardial infarction (MI) is a leading cause of mortality globally and is predominantly attributed to coronary artery disease (CAD). MI is categorized as ST-elevation MI (STEMI) or non-ST-elevation MI (NSTEMI), each with distinct etiologies and treatment pathways. The goal in treatment for both is restoring blood flow back to the myocardium. STEMI, characterized by complete occlusion of a coronary artery, is managed urgently with reperfusion therapy, typically percutaneous coronary intervention (PCI). In contrast, NSTEMI involves a partial occlusion of a coronary artery and is treated with medical management, PCI, or coronary artery bypass grafting (CABG) depending on risk scores and clinical judgment. The Heart Team approach can assist in deciding which reperfusion technique would provide the greatest benefit to the patient and is especially useful in complicated cases. Despite advances in treatment, complications such as cardiogenic shock (CS) and ischemic heart failure (HF) remain significant. While percutaneous coronary intervention (PCI) is considered the primary treatment for MI, it is important to recognize the significance of cardiac surgery in treatment, especially when there is complex disease or MI-related complications. This comprehensive review analyzes the role of cardiac surgery in MI management, recognizing when it is useful, or not.