Results of acute cerebral infarction treatment with hyperbaric oxygen therapy, 2020-2022.

Background: Cerebral stroke is the third leading cause of death after cardiovascular disease, cancer and the leading cause of disability for patients. Hyperbaric oxygen is a non-drug treatment that has the potential to improve brain function for patients with ischaemic stroke. The objective of this study was to evaluate the results of treatment of acute cerebral infarction with hyperbaric oxygen therapy (HBOT).

AI vs. AI: Can AI Detect AI-Generated Images?

The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security.

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation.

Background And Objective: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules.

Typhoid fever, complicated by syncope due to relative bradycardia: A case report.

In a United Nations (UN) staff member headquarters in South Sudan, we present a rare typhoid fever complicated by syncope due to relative bradycardia. A 25-year-old male presented to our hospital with a high fever, diarrhea, and no vomiting. He had no substantial medical background.

Instance-Level Few-Shot Learning With Class Hierarchy Mining.

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to effectively utilizing the relationship between categories. In this paper, we exploit the hierarchical information to leverage discriminative and relevant features of base classes to effectively classify novel objects.

Revisiting natural user interaction in virtual world.

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers.

Multimodal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing.

In this article, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed cross-modal info-max hashing (CMIMH). First, to learn informative representations that can preserve both intramodal and intermodal similarities, we leverage the recent advances in estimating variational lower bound of MI to maximizing the MI between the binary representations and input features and between binary representations of different modalities.

Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite.

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths.

Masked Face Analysis via Multi-Task Deep Learning.

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks.

Intensified salinity intrusion in coastal aquifers due to groundwater overextraction: a case study in the Mekong Delta, Vietnam.

Groundwater salinization is one of the most severe environmental problems in coastal aquifers worldwide, causing exceeding salinity in groundwater supply systems for many purposes. High salinity concentration in groundwater can be detected several kilometers inland and may result in an increased risk for coastal water supply systems and human health problems. This study investigates the impacts of groundwater pumping practices and regional groundwater flow dynamics on groundwater flow and salinity intrusion in the coastal aquifers of the Vietnamese Mekong Delta using the SEAWAT model-a variable-density groundwater flow and solute transport model.

Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation.

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e.

Unsupervised Deep Cross-modality Spectral Hashing.

This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations.

Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning.

Representing images by compact hash codes is an attractive approach for large-scale content-based image retrieval. In most state-of-the-art hashing-based image retrieval systems, for each image, local descriptors are first aggregated as a global representation vector. This global vector is then subjected to a hashing function to generate a binary hash code.

Semantic Prior Analysis for Salient Object Detection.

Salient object detection aims to detect the main objects in the given image. In this paper, we proposed an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data.

Risk factors for anemia in Vietnam.

Anemia is a significant public health problem in Vietnam, but representative national data and comprehensive risk factors analysis are lacking. The objectives of this study were to: 1) determine the distribution and severity of anemia in Vietnam, and 2) to assess potential risk factors for anemia. Nine thousand five hundred fifty households in 53 provinces were covered using a stratified two-stage cluster survey carried out in 1995.