Visual representation and characterization of three-dimensional hydrofracturing cracks within heterogeneous rock through 3D printing and transparent models

The heterogeneity of unconventional reservoir rock tremendously affects its hydrofracturing behavior. A visual representation and accurate characterization of the three-dimensional （3D） growth and distribution of hydrofracturing cracks within heterogeneous rocks is of particular use to the design and implementation of hydrofracturing stimulation of unconventional reservoirs. However, because of the difficulties involved in visually representing and quantitatively characterizing a 3D hydrofracturing crack-network, this issue remains a challenge. In this paper, a novel method is proposed for physically visualizing and quantitatively characterizing the 3D hydrofracturing crack-network distributed through a heterogeneous structure based on a natural glutenite sample. This method incorporates X-ray microfocus computed tomography （μCT）, 3D printing models and hydrofracturing triaxial tests to represent visually the heterogeneous structure, and the 3D crack growth and distribution within a transparent rock model during hydrofracturing. The coupled effects of material heterogeneity and confining geostress on the 3D crack initiation and propagation were analyzed. The results indicate that the breakdown pressure of a heterogeneous rock model is significantly affected by material heterogeneity and confining geostress. The measured breakdown pressures of heterogeneous models are apparently different from those predicted by traditional theories. This study helps to elucidate the quantitative visualization and characterization of the mechanism and influencing factors that determine the hydrofracturing crack initiation and propagation in heterogeneous reservoir rocks.

Visual representation of ASUU strikes in Nigeria:a semiotic analysis of cartoons in selected Nigerian newspapers

Until recently,the study of language and meaning of cartoons,the media discourses they generate,and their analysis as a creative means of exploring meaning-making processes as semiotic resources have not received much scholarly attention in Nigeria.Although cartoons that involve the use of satire and humour as visual representations of reality have gained prominence across many media platforms in Nigeria,only a few scholars have examined this from a social semiotics perspective.This has created a gap in the literature,thus creating room for a para-digm shift in the field of social semiotics.This study explores the semiotics of car-toons in selected Nigerian newspapers to examine the meaning-making resources employed in the visual representation of ASUU strikes in Nigeria.The study,there-fore,examines how cartoonists manipulate symbols,signs,and other semiotic resources to convey specific meanings through visual and textual representations.The study adopts a qualitative research design;data comprising cartoons sourced from selected Vanguard Newspapers and websites are analyzed using Kress and Van Leeuwen's visual semiotics and interpreted from the standpoints of Halliday's Systematic Functional Linguistics(SFL)approach and O'Halloran's position on metaphorical constructions of meaning.Here,metaphorical manipulation and rep-resentation of visual elements in the selected cartoons are interrogated.Findings from the study show the use of semiotic resources in the portrayer of reality in the context of the seemingly intractable ASUU strikes and their consequences on aca-demic activities in Nigeria.This scholarly intervention deserves attention as it significantly contributes to the field of social semiotics through its visual represen-tation in portraying challenges faced by the educational systems in Nigeria vis-a-vis poorgovernment funding.

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Visual representation learning is ubiquitous in various real-world applications,including visual comprehension,video understanding,multi-modal analysis,human-computer interaction,and urban computing.Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era,the lack of interpretability,robustness,and out-of-distribution generalization are becoming the challenges of the existing visual models.The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge,which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities.Inspired by the strong inference ability of human-level agents,recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability.In this paper,we conduct a comprehensive review of existing causal reasoning methods for visual representation learning,covering fundamental theories,models,and datasets.The limitations of current methods and datasets are also discussed.Moreover,we propose some prospective challenges,opportunities,and future research directions for benchmarking causal reasoning algorithms in visual representation learning.This paper aims to provide a comprehensive overview of this emerging field,attract attention,encourage discussions,bring to the forefront the urgency of developing novel causal reasoning methods,publicly available benchmarks,and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.

Masked Autoencoders as Single Object Tracking Learners

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

（The Good）, The Bad and The Ugly： The Visual Construction of Female Child Sex-offenders

This paper explores the visual construction and representation of female sex offenders. It utilises the case study of Vanessa George, a nursery worker who was involved in the exchange of indecent imagery of children via an online paedophile ring. The first part of the paper considers the emergence of the sub-discipline, visual criminology and examines what is known about the visual representation of female offenders. The second part presents the findings of an empirical investigation, which involved engaging in a critical, reflexive visual analysis of a selection photographs and the police mugshot of Vanessa George. The paper considers the ways in which George＇s physical appearance and her suggested ability to deceive were used to visually represent her as ＂other＂, thus reinforcing the existing simplistic motifs of female sex offending.

Visual understanding by mining social media： recent advances and challenges

With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, sev- eral methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media sum- marization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.

Improving Attitude toward Corporate Strategy with Visual Mapping： Scale Development and Application in Europe and China

Companies operating across borders face greater challenges in ensuring compliance with company strategy in different cultural contexts. Recent research shows the cognitive and emotional benefits of utilizing visual representations of knowledge in organizations. This study aims to test if mapping a corporate strategy visually can improve attitudes toward firm strategy and the intention to comply with it. An experiment is conducted comparing two knowledge maps to a textual version of the same company strategy. In order to measure attitudes toward the strategy, a scale was developed and tested in Europe and China. The study outcome provides a parsimonious and effective tripartite scale of attitude with cognitive, affective and behavioral components. The scale was then applied to a different sample to test the effect of mapping a company strategy visually on attitude toward the strategy, and to test if the effect is persistent in Europe and China. The results of the experiment show that subjects exposed to the visual conditions had significantly more positive affective and cognitive attitudes toward the content.

注意力转换实验、准确性条件与表征主义

表征主义者认为知觉经验的现象性完全由表征内容决定。伯劳克反对这一观点,坚持现象意识还包含表征内容无法解释的其它因素,因此并不随附于或等同于表征内容。他的一个重要理由是,主体运用注意力于某个视觉知觉对象会产生不同于未运用注意力于同一对象而产生的现象性。也就是说,诉诸不同程度的注意力,相同的内容可以对应不同的现象意识。在近期的一篇文章中,伯劳克分析了一个关于注意力转换的视觉认知实验,他论证表征主义通常用于回应内容与现象性不一致的两种策略——"内容区分"与"错觉"——都无法解释实验中现象性的差异。本文将论证伯劳克对这一实验的解释不能成功驳倒表征主义。但表征主义者仍面临表征内容作为经验准确性条件的定义问题。