Exemplar Images Research Articles

Women leaders in logistics for the economy 5.0

The complexity of the technologized socioeconomic reality poses a number of challenges for today's managers. Many times their actions are a test of their leadership. A particularly interesting area of research in this regard is the logistics sector, which so far has been regarded as typically male and which has been undergoing a transformation in favor of an increasing representation of women for some years now. The authors sought to answer questions such as: what qualities, skills and competences will enable women working in logistics to pursue their careers and achieve their goals in the face of the challenges of a 5.0 economy. The aim of the article is to identify those leadership skills and competences of women in logistics that predispose them to lead on their way to business success in enterprises. In the research process, mixed research methodologies were used, combining the results of secondary source analysis obtained from Scopus and Web of Science databases with information obtained from a quantitative survey based on the CAWI diagnostic survey method. The analyses resulted in a model of an exemplary image combining traits, skills and competencies, as well as attributes of effectiveness, which may be helpful for women in shaping their leadership position in the management structures of companies in the logistics industry. The resulting material complements the existing body of research on the role and importance of women in business including, in particular, the inclusion of women in logistics. The main strength of the work is to identify areas that women should improve in order to increase their participation in managerial positions in the logistics industry.

Optical design and development of an underwater dual-channel microlens array integral field snapshot hyperspectral imager

Compared to push-scan hyperspectral imagers, snapshot hyperspectral imagers offer an advantage by minimizing sensitivity to attitude jitter in underwater mobile platforms. Here we present the optical design and development of an underwater microlens array integral field hyperspectral imager. The system comprises a panchromatic imaging channel with a high spatial resolution and a spectral imaging channel with a lower spatial resolution. Through the fusion of high-resolution panchromatic images and low-resolution spectral images, we achieve high spatial resolution hyperspectral images. Both the panchromatic imaging channel and the spectral imaging channel share a common front objective, featuring a 25 mm focal length and a wide 36° field of view angle. Utilizing prism dispersion, the spectral imaging system spans a band range from 465 to 700 nm with a spectral resolution of less than 10 nm. Specialized algorithms for spectral image reconstruction and image fusion have been developed. The experimental results across diverse scenes confirm the exemplary spectral imaging performance of the system, positioning it as a robust solution for underwater snapshot hyperspectral imaging.

Technological vigilance and total quality in the management of the mental image of the institution: Goals and bets

Many institutions and companies are working to develop their activities and ensure overall quality. The quality of the service, in particular, aims to satisfy customers and build an exemplary mental image in the minds of this target clientele. Still, the competition market has taken another turn with technological and technical development. The most important of these factors that have developed and that institutions need to work on to build their capabilities is technological vigilance, which is an integral part of the institution's overall strategic plan. Technology must sometimes be selected for quality, especially those that enable sustainable development based on investment in knowledge instead of relying on raw materials. In this research paper, we will focus on the aspects that contribute to overturning the balance of competition in favor of the institution by adopting technological and cognitive mechanisms and knowledge under technological vigilance and maintaining the sustainability of its positive image with customers. Keywords: Technological awakening, knowledge economy, mental image, sharp technologies

WTCRNet: a wavelet transform and contrastive regularization network for sonar denoising by self-supervision

In the field of underwater acoustics, forward-looking sonar represents a pivotal tool for acquiring subaqueous imagery. However, this technique is susceptible to the inherent ambient noise prevalent in underwater environments, resulting in degraded image quality. A notable challenge in this domain is the scarcity of pristine image exemplars, making it difficult to apply many advanced deep denoising networks for the purification of sonar images. To address this issue, the study introduces a novel self-supervised methodology specifically designed for denoising forward-looking sonar images. The proposed model employs a blind-spot network architecture to reconstruct unblemished images. Additionally, it integrates wavelet transform technology within a convolutional neural network (CNN) framework, combining frequency and structural information. Furthermore, the model incorporates contrastive regularization to augment denoising efficiency. This innovative denoising network, which leverages wavelet transform and contrastive regularization (CR), is henceforth referred to as WTCRNet. To evaluate the performance of WTCRNet, this study constructs a dual dataset comprising both simulated and authentic forward-looking sonar images, thereby furnishing a comprehensive dataset for network training and evaluation. Empirical assessments conducted on these datasets demonstrate that WTCRNet substantially outperforms existing denoising methodologies by effectively mitigating noise. The code is available at https://gitee.com/sichengling/wtcrnet.git.

Performing exemplarity: the “two faces” of a village cadre in North China

This article investigates the practice of exemplarity in rural China through Secretary Xu’s oral narratives to provincial leaders and local villagers in a northern model village. It applies a performative narrative analysis to reveal how Xu constructs his persona, utilizing narrative structures, chronotopes, and the interplay of events to forge exemplary images suited to each audience. In presentations to leaders, Xu highlights rural development achievements, while to villagers, he emphasizes common prosperity and community service. This strategic narrative manipulation not only showcases Xu’s skill in aligning with both state and local values but also fosters cultural intimacy and community cohesion despite narrative discrepancies. The case study expands the discourse on narrative techniques and representational strategies within rural revitalization and social governance, offering a nuanced perspective on the intersection of narrative, identity, and governance.

A Study on the Communication Strategy of the Image of Dabu in Zhuang Folk Stories under the Background of the New Era

A Piece of Zhuang Brocade (Yi Fu Zhuang Jin) is a highly representative folk tale of the Zhuang people in Guangxi. The image of the heroine, Dabu, embodies the indomitable pursuit of happiness and the sacrificial spirit of dedication among the Zhuang people, showcasing the unique charm of Chinese traditional folk culture. In the context of the new era, the dissemination and development of Dabu’s image present both opportunities and challenges. Therefore, it is necessary to follow current trends, adhere to the themes and aesthetics of the times, seek ways to highlight Dabu’s character traits and cultural expression, and seize the opportunity to integrate modern elements and culture to promote the exemplary image of Zhuang women.

SATCount: A scale-aware transformer-based class-agnostic counting framework

This paper studies the class-agnostic counting problem, which aims to count objects regardless of their class, and relies only on a limited number of exemplar objects. Existing methods usually extract visual features from query and exemplar images, compute similarity between them using convolution operations, and finally use this information to estimate object counts. However, these approaches often overlook the scale information of the exemplar objects, leading to lower counting accuracy for objects with multi-scale characteristics. Additionally, convolution operations are local linear matching processes that may result in a loss of semantic information, which can limit the performance of the counting algorithm. To address these issues, we devise a new scale-aware transformer-based feature fusion module that integrates visual and scale information of exemplar objects and models similarity between samples and queries using cross-attention. Finally, we propose an object counting algorithm based on a feature extraction backbone, a feature fusion module and a density map regression head, called SATCount. Our experiments on the FSC-147 and the CARPK demonstrate that our model outperforms the state-of-the-art methods.

FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing.

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a novel multimodal generative and fusion framework for globally-consistent local facial editing (FACEMUG) that can handle a wide range of input modalities and enable fine-grained and semantic manipulation while remaining unedited parts unchanged. Different modalities, including sketches, semantic maps, color maps, exemplar images, text, and attribute labels, are adept at conveying diverse conditioning details, and their combined synergy can provide more explicit guidance for the editing process. We thus integrate all modalities into a unified generative latent space to enable multimodal local facial edits. Specifically, a novel multimodal feature fusion mechanism is proposed by utilizing multimodal aggregation and style fusion blocks to fuse facial priors and multimodalities in both latent and feature spaces. We further introduce a novel self-supervised latent warping algorithm to rectify misaligned facial features, efficiently transferring the pose of the edited image to the given latent codes. We evaluate our FACEMUG through extensive experiments and comparisons to state-of-the-art (SOTA) methods. The results demonstrate the superiority of FACEMUG in terms of editing quality, flexibility, and semantic control, making it a promising solution for a wide range of local facial editing tasks.

What Is Peace? The Perspective of People with Cognitive Disabilities in Costa Rica

To address the subject of peace, this paper originates from the generative question, ‘What is peace, and how can it be practiced?’ Targeted at the adult population with cognitive disabilities, the study includes students from the Program for the Inclusion of People with Cognitive Disabilities in Higher Education (PROIN) of the University of Costa Rica. The cognitive impairments they experience are deemed to be “mild” or “medium” since they do not hinder the students’ social progression or physical independence. A focus group was conducted with eight people who examined exemplary images (provided in the annexes). The aim was to understand their perception on how visually they differentiate between what is peace and what is not. From this standpoint, discussions on peace, war, and conflict resolution were developed according to the participants’ perspectives. This paper presents the students’ answers and a final reflection on what they consider to be peace through drawings, photos, images, or their hobbies. The study contributes to understanding the participants' perspectives on the topics of interest, how they deal with conflict situations in their daily lives, and how they seek peace in their families and daily environments.

Hippocampal Functions Modulate Transfer-Appropriate Cortical Representations Supporting Subsequent Memory.

The hippocampus plays a central role as a coordinate system or index of information stored in neocortical loci. Nonetheless, it remains unclear how hippocampal processes integrate with cortical information to facilitate successful memory encoding. Thus, the goal of the current study was to identify specific hippocampal-cortical interactions that support object encoding. We collected fMRI data while 19 human participants (7 female and 12 male) encoded images of real-world objects and tested their memory for object concepts and image exemplars (i.e., conceptual and perceptual memory). Representational similarity analysis revealed robust representations of visual and semantic information in canonical visual (e.g., occipital cortex) and semantic (e.g., angular gyrus) regions in the cortex, but not in the hippocampus. Critically, hippocampal functions modulated the mnemonic impact of cortical representations that are most pertinent to future memory demands, or transfer-appropriate representations Subsequent perceptual memory was best predicted by the strength of visual representations in ventromedial occipital cortex in coordination with hippocampal activity and pattern information during encoding. In parallel, subsequent conceptual memory was best predicted by the strength of semantic representations in left inferior frontal gyrus and angular gyrus in coordination with either hippocampal activity or semantic representational strength during encoding. We found no evidence for transfer-incongruent hippocampal-cortical interactions supporting subsequent memory (i.e., no hippocampal interactions with cortical visual/semantic representations supported conceptual/perceptual memory). Collectively, these results suggest that diverse hippocampal functions flexibly modulate cortical representations of object properties to satisfy distinct future memory demands.Significance Statement The hippocampus is theorized to index pieces of information stored throughout the cortex to support episodic memory. Yet how hippocampal processes integrate with cortical representation of stimulus information remains unclear. Using fMRI, we examined various forms of hippocampal-cortical interactions during object encoding in relation to subsequent performance on conceptual and perceptual memory tests. Our results revealed novel hippocampal-cortical interactions that utilize semantic and visual representations in transfer-appropriate manners: conceptual memory supported by hippocampal modulation of frontoparietal semantic representations, and perceptual memory supported by hippocampal modulation of occipital visual representations. These findings provide important insights into the neural mechanisms underlying the formation of information-rich episodic memory and underscore the value of studying the flexible interplay between brain regions for complex cognition.

Spin-lock based magnetic field sensing: a non-invasive MRI method for spatially resolved imaging of cardiac conduction?

Abstract Introduction We present a novel method enabling spatially resolved detection of ultra-weak magnetic signals by means of standard clinical MRI. For this purpose, we exploit the particular condition of spin-locked magnetisation, which shifts the sensitivity of the MRI device from the MHz to the Hz range [1]. In this state, low-frequency magnetic oscillations lead to rotary excitation (REX) and can even be detected in the nT-range [2]. The following study presents the customisation of this approach to QRS-like fields, with the objective of paving the way for direct imaging of cardiac conduction in humans. Theory: The direct, spatially resolved detection of magnetic fields is accomplished by irradiating a spin-lock (SL) field, which rotates the tissue magnetisation at frequency fSL, followed by a rapid image acquisition. We assume that the interaction of the spin system with a magnetic signal during SL is resonant if the Fourier transform of this signal fits within the range of fSL (Fig. 1a-b). In this case, a sinusoidal NMR signal is expected with a variation of the interaction timing, which is evaluated after imaging and whose amplitude AREX is used for field detection. Methods The new detection principle was validated with externally generated field fluctuations in a phantom experiment on a clinical 3.0T MRI system. In the first step, simple waveforms with well-defined frequency spectra were analysed. AREX was determined from the standard deviation of the observed signal oscillation. By varying fSL, the resonance effect was successively investigated. Subsequently, a QRS-like field whose waveform models MCG measurements was considered [3]. The sequence parameters were optimised for a QS duration of 100ms. Detection was attempted for peak amplitudes in the range 5…100nT. Results Fig. 1c) presents exemplary MR images used for field detection. Fig. 2a-b) show the investigation of the resonance effect for different signals. Magnetic peaks of varying waveforms can be clearly distinguished on the basis of their characteristic response for SL frequencies. Fig. 2c-d) show the results of the first detection trial of an artificial QRS. The observed signal follows a sinusoidal course, which is clearly different from the reference measurement (Fig. 2c). The detected AREX increases linearly (R2>0.99) with the amplitude of the QRS peak (Fig. 2d). Discussion: This work presents a new approach for direct sensing of magnetic signals using MRI. The results demonstrate a high sensitivity in the nT-range, which is sufficient for cardiac fields [4]. Furthermore, spectral properties of magnetic signals can be probed. Therefore, our REX method potentially holds promising applications for non-invasive quantitative diagnosis of cardiac function. However, transfer of the well-controlled phantom experiment to the in vivo setup is expected to face major challenges due to motion and blood flow, and will require superb shimming of the main magnetic field.Figure 1)Concept of QRS detectionFigure 2)Experimental results

Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

Generation of 3D models of victims within their surroundings at rescue sites

Abstract The use of state-of-the-art technology in emergencies enables fast assessment of the situation. Modern rescue robots, but also rescuers with camera systems, record vast amounts of image data. In addition to the benefits for rescue operations, a quick overview based on this data can help medical staff in the search for suitable treatment strategies. However, such data must be examined and fused into an intuitive display. In this work, exemplary image streams of RGB-D cameras are searched for victims and transferred into separate 3D models per detected person for subsequent intuitive viewing, rotating, and zooming. The method was tested and preliminary evaluated for its functionality, detail accuracy and readability of textures at the German Rescue Robotics Center.

Modeling off-axis diffraction with the least-sampling angular spectrum method

Accurately yet efficiently simulating off-axis diffraction is vital to design large-scale computational optics, but existing rigid sampling and modeling schemes fail to address this. Herein, we establish a universal least-sampling angular spectrum method that enables efficient off-axis diffraction modeling with high accuracy. Specifically, by employing the Fourier transform’s shifting property to convert off-axis diffraction to quasi-on-axis, and by linking the angular spectrum to the transfer function, essential sampling requirements can be thoroughly optimized and adaptively determined across computation. Leveraging a flexible matrix-based Fourier transform, we demonstrate the off-axis point spread function of exemplary coded-aperture imaging systems. For the first time, to our knowledge, a significant speed boost of around 36× over the state of the art at 20° is demonstrated, and so is the viability of computing ultra-large angles such as 35° within seconds on a commercial computer. The applicability to high-frequency modulation is further investigated.

One-shot Retinal Artery and Vein Segmentation via Cross-modality Pretraining

To perform one-shot retinal artery and vein segmentation with cross-modality artery-vein (AV) soft-label pretraining. Cross-sectional study. The study included 6479 color fundus photography (CFP) and arterial-venous fundus fluorescein angiography (FFA) pairs from 1964 participants for pretraining and 6 AV segmentation data sets with various image sources, including RITE, HRF, LES-AV, AV-WIDE, PortableAV, and DRSplusAV for one-shot finetuning and testing. We structurally matched the arterial and venous phase of FFA with CFP, the AV soft labels were automatically generated by utilizing the fluorescein intensity difference of the arterial and venous-phase FFA images, and the soft labels were then used to train a generative adversarial network to learn to generate AV soft segmentations using CFP images as input. We then finetuned the pretrained model to perform AV segmentation using only one image from each of the AV segmentation data sets and test on the remainder. To investigate the effect and reliability of one-shot finetuning, we conducted experiments without finetuning and by finetuning the pretrained model on an iteratively different single image for each data set under the same experimental setting and tested the models on the remaining images. The AV segmentation was assessed by area under the receiver operating characteristic curve (AUC), accuracy, Dice score, sensitivity, and specificity. After the FFA-AV soft label pretraining, our method required only one exemplar image from each camera or modality and achieved similar performance with full-data training, with AUC ranging from 0.901 to 0.971, accuracy from 0.959 to 0.980, Dice score from 0.585 to 0.773, sensitivity from 0.574 to 0.763, and specificity from 0.981 to 0.991. Compared with no finetuning, the segmentation performance improved after one-shot finetuning. When finetuned on different images in each data set, the standard deviation of the segmentation results across models ranged from 0.001 to 0.10. This study presents the first one-shot approach to retinal artery and vein segmentation. The proposed labeling method is time-saving and efficient, demonstrating a promising direction for retinal-vessel segmentation and enabling the potential for widespread application. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-Realistic Style Transfer

Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods are too expensive to run in real-time. Meanwhile, bilateral grid based methods are much faster but still contain artifacts like overexposure. In this work, we propose the Adaptive ColorMLP (AdaCM), an effective and efficient framework for universal photo-realistic style transfer. First, we find the complex non-linear color mapping between input and target domain can be efficiently modeled by a small multi-layer perceptron (ColorMLP) model. Then, in AdaCM, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair. Experimental results demonstrate that AdaCM can generate vivid and high-quality stylization results. Meanwhile, our AdaCM is ultrafast and can process a 4K resolution image in 6ms on one V100 GPU.

Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning

AbstractA key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.

Attention guided domain alignment for conditional face image generation

Despite the great success of Generative Adversarial Network (GAN) in face image generation, it is still a challenge to generate a faithful yet high-fidelity face image given an exemplar image and a conditional input from a distinct domain (e.g., a semantic segmentation mask or an edge map). Existing methods learn image-level features to align distinct domains in an intermediate domain, which ignore the spatial relationship of the facial semantic parts and therefore lead to semantic mismatching problems. In addition, it is computationally expensive to establish dense correspondences in the intermediate domain, especially for high-resolution face images. To address these problems, we propose a novel attention guided domain alignment method for conditional face image generation, which aligns two domains directly under the guidance of the local attention learned from semantically similar face parts. In particular, we assign a dedicated index for each feature block and adopt a top-k ranking operation to correspond block-wise features in two distinct domains, which exploits the spatial relationship of facial parts and preserves the texture structure during alignment. The local attention is then learned from the retrieved blocks, which reduces computation complexity substantially and therefore enables to build alignment in a high resolution. The aligned features are finally fused with adaptive weights learned from their long-range correlation coefficients, which capture the semantic coherence of the style features between the two domains. Extensive experimental results on the CelebAMask-HQ dataset demonstrate that the proposed method is superior to the state-of-the-art methods.

High-quality color image restoration from a disturbed graded-index imaging system by deep neural networks.

Imaging transmission plays an important role in endoscopic clinical diagnosis involved in modern medical treatment. However, image distortion due to various reasons has been a major obstacle to state-of-art endoscopic development. Here, as a preliminary study we demonstrate ultra-efficient recovery of exemplary 2D color images transmitted by a disturbed graded-index (GRIN) imaging system through the deep neural networks (DNNs). Indeed, the GRIN imaging system can preserve analog images through the GRIN waveguides with high quality, while the DNNs serve as an efficient tool for imaging distortion correction. Combining GRIN imaging systems and DNNs can greatly reduce the training process and achieve ideal imaging transmission. We consider imaging distortion under different realistic conditions and use both pix2pix and U-net type DNNs to restore the images, indicating the suitable network in each condition. This method can automatically cleanse the distorted images with superior robustness and accuracy, which can potentially be used in minimally invasive medical applications.

On the importance of patient acceptance for medical robotic imaging

PurposeMutual acceptance is required for any human-to-human interaction. Therefore, one would assume that this also holds for robot–patient interactions. However, the medical robotic imaging field lacks research in the area of acceptance. This work, therefore, aims at analyzing the influence of robot–patient interactions on acceptance in an exemplary medical robotic imaging system.MethodsWe designed an interactive human-robot scenario, including auditive and gestural cues, and compared this pipeline to a non-interactive scenario. Both scenarios were evaluated through a questionnaire to measure acceptance. Heart rate monitoring was also used to measure stress. The impact of the interaction was quantified in the use case of robotic ultrasound scanning of the neck.ResultsWe conducted the first user study on patient acceptance of robotic ultrasound. Results show that verbal interactions impacts trust more than gestural ones. Furthermore, through interaction, the robot is perceived to be friendlier. The heart rate data indicates that robot–patient interaction could reduce stress.ConclusionsRobot–patient interactions are crucial for improving acceptance in medical robotic imaging systems. While verbal interaction is most important, the preferred interaction type and content are participant dependent. Heart rate values indicate that such interactions can also reduce stress. Overall, this initial work showed that interactions improve patient acceptance in medical robotic imaging, and other medical robot–patient systems can benefit from the design proposals to enhance acceptance in interactive scenarios.