semantics

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently...

Despite its significant progress, cross-modal retrieval still suffers from one-to-many matching cases, where the multiplicity of semantic instances in another modality could be acquired by a given query. However, existing approaches usually map heterogeneous data into the learned space as deterministic point vectors. In spite of their remarkable performance in matching the most similar instance, such deterministic point embedding suffers from the insufficient representation of rich semantics in one-to-many correspondence...

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node...

In zero-shot learning (ZSL), attribute knowledge plays a vital role in transferring knowledge from seen classes to unseen classes. However, most existing ZSL methods learn biased attribute knowledge, which usually results in biased attribute prediction and a decline in zero-shot recognition performance. To solve this problem and learn unbiased attribute knowledge, we propose a visual attribute Transformer for zero-shot recognition (ZS-VAT), which is an effective and interpretable Transformer designed specifically for ZSL...

Cross-modal hashing (CMH) has attracted considerable attention in recent years. Almost all existing CMH methods primarily focus on reducing the modality gap and semantic gap, i.e., aligning multi-modal features and their semantics in Hamming space, without taking into account the space gap, i.e., difference between the real number space and the Hamming space. In fact, the space gap can affect the performance of CMH methods. In this paper, we analyze and demonstrate how the space gap affects the existing CMH methods, which therefore raises two problems: solution space compression and loss function oscillation...

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object-scene relations (which objects are expected in a given scene) and object-object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers' attention (the semantic-inconsistency effect ). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs...

BACKGROUND: Vaccine hesitancy among parents directly affects the child's vaccination status since they are the legal decision-makers regarding vaccinating their children. The study aimed to describe the perceptions of highly educated Kazakhstani mothers about childhood vaccination hesitancy. METHODS: The study utilized a thematic analysis to explore the mothers' perceptions. A sample of 95 participants comprehensively answered the free-text questions in an online questionnaire from January to February 2023...

Introduction Influenza is a major global health concern, with its rapid spread and mutation rate posing significant challenges in public health education and communication. Effective patient education materials (PEMs) are crucial for informed decision-making and improved health outcomes. This study evaluates the efficacy of online influenza PEMs using traditional readability tools and introduces the Contextual Health Education Readability Score (CHERS) to address the limitations of existing methods that do not capture the diverse array of visual and thematic means displayed...

PURPOSE: To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msubsup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn> <mml:mo>*</mml:mo></mml:msubsup> </mml:mrow> <mml:annotation>$$ {\mathrm{R}}_2^{\ast } $$</mml:annotation></mml:semantics> </mml:math> quantification accelerated by radial k-space undersampling...

PURPOSE: This work reports for the first time on the implementation and application of cardiac diffusion-weighted MRI on a Connectom MR scanner with a maximum gradient strength of 300 mT/m. It evaluates the benefits of the increased gradient performance for the investigation of the myocardial microstructure. METHODS: Cardiac diffusion-weighted imaging (DWI) experiments were performed on 10 healthy volunteers using a spin-echo sequence with up to second- and third-order motion compensation ( <mml:math xmlns:mml="https://www...

BACKGROUND: In patients with coronary heart disease (CHD), individualized exercise training (ET) programs are strongly recommended to optimize peak oxygen uptake ( <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:mover><mml:mi>V</mml:mi> <mml:mo>̇</mml:mo></mml:mover> </mml:mrow> <mml:annotation>$$ \dot{\mathrm{V}} $$</mml:annotation></mml:semantics> </mml:math> O2peak ) improvement and prognosis...

PURPOSE: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. METHODS: Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated...

INTRODUCTION: Bleeding severity in severe haemophilic patients, with low thrombin generation (TG) capacity, can vary widely between patients, possibly reflecting differences in tissue factor pathway inhibitor (TFPI) level. AIM: To compare free TFPI (fTFPI) levels in patients with severe haemophilia A (sHA) and severe haemophilia B (sHB) and to investigate in these patients as a whole the relationships between bleeding and TG potential, between TG potential and fTFPI level and between fTFPI level and bleeding tendency...

PURPOSE: CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. METHODS: Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0...

The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that draws from statements of ignorance (SOIs) found in the research literature. By building researchers' profiles based on textual elements, RecSOI generates personalized recommendations of potential research directions tailored to their interests...

Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermore, the computational cost of inspecting each pixel is expensive because the number of pixels is high even at low resolution. In this work, we propose a hybrid image processing method. Simple Linear Iterative Clustering with Gaussian Filter (SLIC-G) for the purpose of overcoming pixel constraints...

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process...

BACKGROUND AND PURPOSE: We examined the allocation of visual attention in an association task by both neurotypical participants (n = 11) and adults with fluent aphasia (n = 12). RESEARCH DESIGN: Participants were presented with a picture (e.g., a basket) followed by a semantically related association (i.e., "strawberry") and an orthographically related association (i.e., "b," the first letter of "basket"). ANALYSIS: An eye tracker recorded their eye movements for three areas of interest (AOI): the picture, the semantic associate, and the orthographic associate, over 1396 observations...

&#xD;Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis (ALS)...

PURPOSE: Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms...