Salient Information Research Articles

The effects of yohimbine and hydrocortisone on selective attention to fearful faces: An fMRI study

IntroductionSelective attention to salient emotional information can enable an advantage in the face of danger. The present study aims to investigate the influence of the stress neuromodulators, norepinephrine and cortisol, on selective attention processes to fearful faces and its neuronal activation. Methods and materialsWe used a randomized, double-blind, placebo-controlled design. 167 healthy men between 18 and 35 years (mean [SD] age: 25.23 [4.24] years) participated in the study. Participants received either: (A) yohimbine (n= 41), (B) hydrocortisone (n = 41), (C) yohimbine and hydrocortisone (n = 42) or (D) placebo only (n= 43) and participated in a dot-probe task with fearful and neutral faces in an fMRI scanner. ResultsWe found an attentional bias toward fearful faces across all groups and related neuronal activation in the left cuneus. We did not find any differences between experimental treatment groups in selective attention and its neuronal activation. DiscussionOur results provide evidence that fearful faces lead to an attentional bias with related neuronal activation in the left cuneus. We did not replicate formerly reported activation in the amygdala, intraparietal sulcus, dorsal anterior cingulate cortex, and thalamus. Suitability of the dot-probe task for fMRI studies and insignificant treatment effects are discussed.

Polarization image fusion method based on polarization saliency with generator adversarial network

Aiming at the problem that the polarization image fusion process does not make good use of the polarization imaging characteristics, we propose a polarization image fusion method based on polarization saliency. On the one hand, we propose a new definition of physical property characterization based on polarization properties:polarization saliency. We construct it from three perspectives: image difference, position, and contrast. On the other hand, we propose a polarization saliency based fusion network (PSGGAN). The information decision block is constructed to enhance the polarization salient information and texture detail information of the source image. The loss function based on polarization saliency is designed to regulate the pixel distribution constraints and the degree of polarization information preservation. Qualitative and quantitative experiments demonstrate the superiority of our PSGGAN over state-of-the-art methods in terms of visual effects and quantitative metrics.

Top–down and bottom–up information acquisition: Application to financial markets

Recent studies confirm the main prediction of the rational inattention framework that the perceived value of information (top–down) as a key driver of attention. However, these investigations also report stimulus-driven salience effects (bottom–up) that counteract the framework’s predictions. In this manuscript, I propose an extension to the standard rational inattention model by incorporating bottom–up attention processes such as salience-effects through varying information processing costs. Applied to asset pricing with a representative agent a higher information salience generally reduces the cost of information processing (attention) needed for the same level of uncertainty reduction. Due to a substitution effect in the attention allocation across information, an attention-maximizing salience emerges. In general, a higher information salience consistently enhances asset price informativeness.

Discourse Effect on Children’s Interpretation of Pre-Subject Exclusive Zhiyou ‘Only’

ABSTRACT It has been reported for decades that preschool children (age 4-7) tend to assign non-adult-like interpretations for sentences with pre-subject exclusive only. This study reports findings from two experiments investigating (1) the effects of (in)congruent implicit questions in discourse contexts and (2) word order transformation on children’s interpretations of Mandarin pre-subject zhiyou ‘only’. Experiment 1 showed that children performed non-adult-like in the incongruent discourse condition, similar to the patterns found in previous studies, but adult-like in the congruent discourse condition. Experiment 2 showed that using sentences similar to English ‘Those who ate apples was only the boy’ where the VP precedes the subject NP with zhiyou ‘only’ was not sufficient to overcome the processing difficulty caused by the incongruent discourse for preschool children. The results demonstrate that a congruent context taking the VP as the background (i.e., given and salient information) in Experiment 1, but not the syntactic manipulation in Experiment 2, is sufficient for children to interpret sentences with pre-subject zhiyou ‘only’ at an adult-like level of accuracy. The findings demonstrate that children have the syntactic knowledge for sentences with exclusive zhiyou ‘only’, but the discourse incongruence hinders them from correctly interpreting sentences with exclusive zhiyou ‘only’ when it is in pre-subject position.It has been reported for decades that preschool children (age 4-7) tend to assign non-adult-like interpretations for sentences with pre-subject exclusive only. This study reports findings from two experiments investigating (1) the effects of (in)congruent implicit questions in discourse contexts and (2) word order transformation on children’s interpretations of Mandarin pre-subject zhiyou ‘only’. Experiment 1 showed that children performed non-adult-like in the incongruent discourse condition, similar to the patterns found in previous studies, but adult-like in the congruent discourse condition. Experiment 2 showed that using sentences similar to English ‘Those who ate apples was only the boy’ where the VP precedes the subject NP with zhiyou ‘only’ was not sufficient to overcome the processing difficulty caused by the incongruent discourse for preschool children. The results demonstrate that a congruent context taking the VP as the background (i.e., given and salient information) in Experiment 1, but not the syntactic manipulation in Experiment 2, is sufficient for children to interpret sentences with pre-subject zhiyou ‘only’ at an adult-like level of accuracy. The findings demonstrate that children have the syntactic knowledge for sentences with exclusive zhiyou ‘only’, but the discourse incongruence hinders them from correctly interpreting sentences with exclusive zhiyou ‘only’ when it is in pre-subject position.

Evaluating Natural Language Processing Packages for Predicting Hospital-Acquired Pressure Injuries From Clinical Notes.

Incidence of hospital-acquired pressure injury, a key indicator of nursing quality, is directly proportional to adverse outcomes, increased hospital stays, and economic burdens on patients, caregivers, and society. Thus, predicting hospital-acquired pressure injury is important. Prediction models use structured data more often than unstructured notes, although the latter often contain useful patient information. We hypothesize that unstructured notes, such as nursing notes, can predict hospital-acquired pressure injury. We evaluate the impact of using various natural language processing packages to identify salient patient information from unstructured text. We use named entity recognition to identify keywords, which comprise the feature space of our classifier for hospital-acquired pressure injury prediction. We compare scispaCy and Stanza, two different named entity recognition models, using unstructured notes in Medical Information Mart for Intensive Care III, a publicly available ICU data set. To assess the impact of vocabulary size reduction, we compare the use of all clinical notes with only nursing notes. Our results suggest that named entity recognition extraction using nursing notes can yield accurate models. Moreover, the extracted keywords play a significant role in the prediction of hospital-acquired pressure injury.

Meeting Summarization: A Survey

Abstract: In an era characterized by an incessant influx of information, the demand for efficient knowledge extraction tools has become paramount. This research introduces a Meeting Sum- marizer, a cutting-edge system that amalgamates speech recog- nition and natural language processing (NLP) to autonomously distill salient information from recorded meetings. The primary objective of this research is to alleviate the cumbersome process of manual meeting summarization by harnessing the capabilities of advanced machine learning techniques tailored to audio data. The Meeting Summarizer leverages state-of-theart speech recognition algorithms to transcribe spoken content into textual form, laying the foundation for subsequent NLPbased analysis. Through the integration of deep learning methodologies, the system discerns key discussions, identifies critical components, and extracts context from meeting transcripts. The synergy of speech recognition and NLP empowers the system to comprehend linguistic nuances, enabling it to adapt to diverse meeting contexts.

Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City

ABSTRACT Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors.

HATF: Multi-Modal Feature Learning for Infrared and Visible Image Fusion via Hybrid Attention Transformer

Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. To extract rich structural features at different scales, residual U-Nets with mixed receptive fields are adopted to capture salient object information at various granularities. Then, a hybrid attention fusion strategy is employed to integrate the complementing information from the input images. Finally, adaptive loss functions are designed to achieve optimal fusion results for different modal features. The fusion framework proposed in this study is thoroughly evaluated using the TNO, FLIR, and LLVIP datasets, encompassing diverse scenes and varying illumination conditions. In the comparative experiments, HATF achieved competitive results on three datasets, with EN, SD, MI, and SSIM metrics reaching the best performance on the TNO dataset, surpassing the second-best method by 2.3%, 18.8%, 4.2%, and 2.2%, respectively. These results validate the effectiveness of the proposed method in terms of both robustness and image fusion quality compared to several popular methods.

Contamination fear and attention bias variability early in the COVID-19 pandemic

The onset of the COVID-19 pandemic resulted in a dramatic increase in the salience and importance of information relating to both the risk of infection, and factors that could mitigate against such risk. This is likely to have contributed to elevated contamination fear concerns in the general population. Biased attention for contamination-related information has been proposed as a potential mechanism underlying contamination fear, though evidence regarding the presence of such biased attention has been inconsistent. A possible reason for this is that contamination fear may be characterised by variability in attention bias that has not yet been examined. The current study examined the potential association between attention bias variability for both contamination-related and mitigation-related stimuli, and contamination fear during the early stages of the COVID-19 pandemic. A final sample of 315 participants completed measures of attention bias and contamination fear. The measure of average attention bias for contamination-related stimuli and mitigation-related stimuli was not associated with contamination fear (r = 0.055 and r = 0.051, p > 0.10), though both attention bias variability measures did show a small but statistically significant relationship with contamination fear (r = 0.133, p < 0.05; r = 0.147, p < 0.01). These attention bias variability measures also accounted for significant additional variance in contamination fear above the average attention bias measure (and controlling for response time variability). These findings provide initial evidence for the association between attention bias variability and contamination fear, underscoring a potential target for cognitive bias interventions for clinical contamination fear.

Seizing and freezing to life outcomes: Need for cognitive closure intensifies affective reactions to major events

Research shows that significant well-being disparities emerge between individuals who experience major negative events and those who undergo major positive events. However, such differences may vary among individuals. Drawing from the theory of the need for cognitive closure (NFC), we theorize and test whether NFC, which captures seizing and freezing on salient information to reach a fast conclusion, intensifies the differences in affective well-being between those who experience negative or positive events. Across three studies (total N = 2,399), we provide converging evidence that supports our theoretical claim. We first found that participants with high (vs. low) NFC show a greater affective well-being gap between those who recalled their past negative and positive major events (Study 1). We also discovered consistent patterns when participants were provided with negative or positive major event scenarios (Study 2). Lastly, we further substantiated our findings by utilizing a longitudinal study of the 20th presidential election in South Korea (Study 3). Over a 6-week period, the well-being gaps between the supporters of the election winner and the runner-up were more pronounced among individuals with higher NFC. Overall, our findings have implications for identifying a novel psychological trait that influences the affective well-being gaps following significant events in one’s life.

First encounters: Estimating the initial magnitude of attentional capture

ABSTRACT Salient but irrelevant information often captures our attention. To quantify attentional capture in the lab, participants typically complete dozens or hundreds of trials that contain salient distractors. However, presenting distractors frequently may also incidentally introduce a secondary task-set to resist distraction. In everyday life, we typically encounter distractions once, not dozens of times, creating a disconnect between capture in the lab and in the world. Here, we had a simple but important question: how large is capture when participants encounter a salient distractor for the very first time? We used a larger-than-typical sample size combined with a single capture trial (Exp 1 N = 970, Exp 2 N = 1,025), and found that initial capture was ∼9x–15x larger than the average size of capture in typical laboratory tasks (∼370–580 ms). We also found evidence that initial capture was modulated by long- and short-term stimulus history, consistent with emerging theories about how history shapes attention. Our results suggest that participants are surprisingly successful at ignoring distractors in the lab, and that “one-shot” estimates may better reflect the full cost of capture by salient distractors.

Fusion of Single and Integral Multispectral Aerial Images

An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation.

A Deep Learning Method for Building Extraction from Remote Sensing Images by Fuzing Local and Global Features

As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.

Nudging towards COVID-19 and influenza vaccination uptake in medically at-risk children: EPIC study protocol of randomised controlled trials in Australian paediatric outpatient clinics

IntroductionChildren with chronic medical diseases are at an unacceptable risk of hospitalisation and death from influenza and SARS-CoV-2 infections. Over the past two decades, behavioural scientists have learnt how to...

Polarization motivating high-performance weak targets' imaging based on a dual-discriminator GAN.

High-level detection of weak targets under bright light has always been an important yet challenging task. In this paper, a method of effectively fusing intensity and polarization information has been proposed to tackle this issue. Specifically, an attention-guided dual-discriminator generative adversarial network (GAN) has been designed for image fusion of these two sources, in which the fusion results can maintain rich background information in intensity images while significantly completing target information from polarization images. The framework consists of a generator and two discriminators, which retain the texture and salient information as much as possible from the source images. Furthermore, attention mechanism is introduced to focus on contextual semantic information and enhance long-term dependency. For preserving salient information, a suitable loss function has been introduced to constrain the pixel-level distribution between the result and the original image. Moreover, the real scene dataset of weak targets under bright light has been built and the effects of fusion between polarization and intensity information on different weak targets have been investigated and discussed. The results demonstrate that the proposed method outperforms other methods both in subjective evaluations and objective indexes, which prove the effectiveness of achieving accurate detection of weak targets in bright light background.

ERTNet: an interpretable transformer-based framework for EEG emotion recognition.

Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy. We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state. Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data. Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.

Dynamical Network Stability Analysis of Multiple Biological Ages Provides a Framework for Understanding the Aging Process.

Widespread interest in nondestructive biomarkers of aging has led to a multitude of biological ages that each proffers a "true" health-adjusted individual age. Although each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging, which are explicitly multidimensional, multicausal, and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behavior of the system as a whole can then be explored using dynamical network stability analysis, which identifies new, efficient biomarkers that quantify long-term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow-on the timescale of a human lifespan. This slow direction provides an aging biomarker, which correlates strongly with chronological age and predicts longitudinal decline in health-suggesting that it estimates an important driver of age-related changes.

Assessing transnational spillover effects of Mexico's front-of-package nutritional labeling system among Mexican Americans in the US

ObjectiveIn 2020, Mexico implemented innovative front-of-package nutrition warning labels (FoPWLs) for packaged foods to increase the salience and understanding of nutrition information. This study evaluated Mexican Americans' self-reported exposure to Mexican FoPWLs and self-reported effects of FoPWLs on purchasing behavior. MethodsThe 2021 International Food Policy Study surveyed online panels of adult Mexican Americans in the US (n = 3361) to self-report on buying food at Mexican-oriented stores, noticing Mexican FoPWLs, and being influenced by FoPWLs to purchase less of eight different unhealthy foods (each assessed separately). After recoding the frequency of buying foods in Mexican stores and noticing FoPWLs (i.e., “often” or “very often” vs. less often), logistic models regressed these outcomes on sociodemographics, adjusting for post-stratification weights. ResultsMost participants (88.0%) purchased foods in Mexican stores. Of these, 64.1% reported noticing FoPWLs, among whom many reported that FoPWLs influenced them to buy fewer unhealthy foods (range = 32% [snacks like chips] - 44% [colas]). Participants were more likely to buy foods in Mexican stores and notice FoPWLs if they were younger, had ≥two children at home vs no children (AOR = 1.40, 95%CI = 1.15–1.71; AOR = 1.37, 95%CI = 1.03–1.80, respectively), and more frequently used Spanish (AOR = 1.91, 95%CI = 1.77–2.07; AOR = 1.87, 95%CI = 1.69–2.07). Also, high vs. low education (AOR = 1.51, 95%CI = 1.17–1.94) and higher income adequacy (AOR = 1.37, 95%CI = 1.25–1.51) were positively associated with noticing FoPWLs. Being female and more frequent Spanish use were consistently associated with reporting purchase of fewer unhealthy foods because of FoPWLs. ConclusionsMany Mexican Americans report both exposure to Mexican FOPWLs and reducing purchases of unhealthy foods because of them.

Cross-Modal Retrieval and Semantic Refinement for Remote Sensing Image Captioning

Two-stage remote sensing image captioning (RSIC) methods have achieved promising results by incorporating additional pre-trained remote sensing tasks to extract supplementary information and improve caption quality. However, these methods face limitations in semantic comprehension, as pre-trained detectors/classifiers are constrained by predefined labels, leading to an oversight of the intricate and diverse details present in remote sensing images (RSIs). Additionally, the handling of auxiliary remote sensing tasks separately can introduce challenges in ensuring seamless integration and alignment with the captioning process. To address these problems, we propose a novel cross-modal retrieval and semantic refinement (CRSR) RSIC method. Specifically, we employ a cross-modal retrieval model to retrieve relevant sentences of each image. The words in these retrieved sentences are then considered as primary semantic information, providing valuable supplementary information for the captioning process. To further enhance the quality of the captions, we introduce a semantic refinement module that refines the primary semantic information, which helps to filter out misleading information and emphasize visually salient semantic information. A Transformer Mapper network is introduced to expand the representation of image features beyond the retrieved supplementary information with learnable queries. Both the refined semantic tokens and visual features are integrated and fed into a cross-modal decoder for caption generation. Through extensive experiments, we demonstrate the superiority of our CRSR method over existing state-of-the-art approaches on the RSICD, the UCM-Captions, and the Sydney-Captions datasets

Cross-Domain State-of-Charge Estimation of Li-Ion Batteries Based on Source Domain Selection and Attention Mechanism

In the domain of lithium-ion (Li-ion) battery state-of-charge (SOC) estimation, deep neural network models commonly assume a congruent distribution between training and testing data. Nonetheless, this assumption often proves inadequate in real-world scenarios, due to variations in environmental temperature, aging levels, and operational conditions. To tackle this challenge, this study proposes a novel approach centered around a deep transfer network, incorporating source domain selection and an attention mechanism, for the task of cross-domain SOC estimation. This approach leverages a transfer network to extract bidirectional temporal features and accentuate salient information within sequences. The selection of an appropriate source domain for pretraining is contingent upon establishing domain similarity between the source and target domains. Experimental results demonstrate that the proposed method excels at feature extraction from Libs sequence data, yielding enhanced performance even when confronted with limited data.