Visual Template Research Articles

Enhancing the Autonomy of Establishing Indicator Informatization.

Quality indicators are essential for hospital management, clinical decision-making, and patient care. In today's technologically advanced era, it is increasingly critical to derive effective statistical data analysis from intricate datasets. However, the administrative department within our hospital often fails to prioritize information needs due to limited resources. Consequently, the collection of indicator data continues to rely on paper-based operations.To tackle common issues in collecting indicator data, such as incomplete or inaccurate data, time-consuming aggregation and statistical analysis, and a lack of timeliness, we have implemented appropriate Business Intelligence (BI) tools. We provide educational training to empower users to create visual templates for indicators that are tailored to their specific requirements. This approach saves time, reduces data duplication, and minimizes writing errors. It enhances supervisors' understanding of the current situation and future prospects in terms of data analysis and timeliness. Ultimately, this initiative improves healthcare quality and management effectiveness.

Influence of gaze, vision, and memory on hand kinematics in a placement task.

People usually reach for objects to place them in some position and orientation, but the placement component of this sequence is often ignored. For example, reaches are influenced by gaze position, visual feedback, and memory delays, but their influence on object placement is unclear. Here, we tested these factors in a task where participants placed and oriented a trapezoidal block against two-dimensional (2-D) visual templates displayed on a frontally located computer screen. In experiment 1, participants matched the block to three possible orientations: 0° (horizontal), +45° and -45°, with gaze fixated 10° to the left/right. The hand and template either remained illuminated (closed-loop), or visual feedback was removed (open-loop). Here, hand location consistently overshot the template relative to gaze, especially in the open-loop task; likewise, orientation was influenced by gaze position (depending on template orientation and visual feedback). In experiment 2, a memory delay was added, and participants sometimes performed saccades (toward, away from, or across the template). In this task, the influence of gaze on orientation vanished, but location errors were influenced by both template orientation and final gaze position. Contrary to our expectations, the previous saccade metrics also impacted placement overshoot. Overall, hand orientation was influenced by template orientation in a nonlinear fashion. These results demonstrate interactions between gaze and orientation signals in the planning and execution of hand placement and suggest different neural mechanisms for closed-loop, open-loop, and memory delay placement.NEW & NOTEWORTHY Eye-hand coordination studies usually focus on object acquisition, but placement is equally important. We investigated how gaze position influences object placement toward a 2-D template with different levels of visual feedback. Like reach, placement overestimated goal location relative to gaze and was influenced by previous saccade metrics. Gaze also modulated hand orientation, depending on template orientation and level of visual feedback. Gaze influence was feedback-dependent, with location errors having no significant effect after a memory delay.

Abstract 5506: HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment

Abstract Solid tumor complexity extends beyond the genetic and functional landscapes of heterogenous cancer cells, encompassing the tumor microenvironment (TME). Elucidating the TME’s complexity requires a comprehensive assessment of its cellular composition, functional states, and spatial distributions. We developed the Harmonic Output of Stromal Traits (HOST) to identify TME cells, and the HOST-Factor to quantify their functional states. The HOST-Factor is a numerical value that reflects the relative contribution of cancer-associated fibroblasts (CAFs) to tumor-suppressive or tumor-promoting functions. Our workflow combines automated cycling highplex immunofluorescent microscopy with artificial intelligence (AI)-guided image analysis. This generates HOST-Factor values for each identified TME cell within selected regions of interest, providing spatial distribution data. The TME signature encompasses 15 immune cells and 14 CAF antibody-detected biomarkers. We applied our workflow to ten human pancreatic cancer specimens, generating OME-TIFF output images. This cancer model was used due to its significant TME makeup. The 29 highplex AI-based digital image analysis was conducted using the Phenoplex™ workflow from Visiopharm. The workflow included deep-learning-based tissue morphologic and cellular segmentations, cellular phenotyping, and integration of spatial/location data. Biomarker subsets were visualized, and a user-trained algorithm was used for tissue segmentation. Nuclear segmentation was done using a pre-trained algorithm on a DAPI-labeled DNA channel. Cellular phenotyping was performed using thresholds based on visual assessment and confirmation of positivity. Spatial neighborhood plots and metrics, heatmaps and partitioned t-SNE plots were generated for the dataset for downstream analysis. Importantly, the workflow's visualization templates, pre-trained nuclear/cytoplasm segmentation tools, and neighborhood plots and metrics, are reusable and fully customizable for new datasets. Using HOST-Factor values, we successfully classified cancer and TME cells, along with their functional states, and spatial distributions. This AI-based computational approach and user-friendly workflow provides a simple and effective way to obtain single-cell information from multiplex immunofluorescence images, making spatial phenotyping of several cell populations in tissues more accessible to researchers, providing a fully amendable means for future clinical translation. Citation Format: Janusz Franco-Barraza, Fabian Schneider, Rasmus Norré-Sørensen, Rasmus Ahrenkiel-Lyngby, Caneta Brown, Dan Winkowski, James Robert Mansfield, Edna Cukierman. HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5506.

LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline TP53 Mutations.

LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the TP53 gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components and further visualize the risk profiles of their patients to aid the decision-making process. LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.

Multiple mechanisms of visual prediction as revealed by the timecourse of scene-object facilitation.

Not only semantic, but also recently learned arbitrary associations have the potential to facilitate visual processing in everyday life-for example, knowledge of a (moveable) object's location at a specific time may facilitate visual processing of that object. In our prior work, we showed that previewing a scene can facilitate processing of recently associated objects at the level of visual analysis (Smith and Federmeier in Journal of Cognitive Neuroscience, 32(5):783-803, 2020). In the current study, we assess how rapidly this facilitation unfolds by manipulating scene preview duration. We then compare our results to studies using well-learned object-scene associations in a first-pass assessment of whether systems consolidation might speed up high-level visual prediction. In two ERP experiments (N = 60), we had participants study categorically organized novel object-scene pairs in an explicit paired associate learning task. At test, we varied contextual pre-exposure duration, both between (200 vs. 2500 ms) and within subjects (0-2500 ms). We examined the N300, an event-related potential component linked to high-level visual processing of objects and scenes and found that N300 effects of scene congruity increase with longer scene previews, up to approximately 1-2 s. Similar results were obtained for response times and in a separate component-neutral ERP analysis of visual template matching. Our findings contrast with prior evidence that scenes can rapidly facilitate visual processing of commonly associated objects. This raises the possibility that systems consolidation might mediate different kinds of predictive processing with different temporal profiles.

Semiotic Organization of Russian Environmental Internet Memes

In this article, the authors examine Russian environmental internet memes, presented on social networks such as VKontakte, Odnoklassniki, and on websites like Memepedia, Pikabu, and A. Chistopashin’s site. They have developed a set of criteria relevant for the analysis of environmental internet memes as complex linguistic and semiotic formations. The aim of the study was to identify the specific features of the semiotic organization of environmental internet memes and establish the dominant semiotic code that determines the conceptual content of the meme. It is shown that the Russian-speaking segment of environmental communication through memes has its own thematic and genre specificity. Environmental internet memes usually rely on the use of media semiotic codes, where the meme includes both text and illustration. Less frequently, memes are created based on narrative semiotic codes, where the visual component illustrates the beginning, climax, or end of a story. The verbal component is dominant when borrowing viral visual templates. The main stylistic device in environmental internet memes, alongside irony and the effect of false expectation, is linguistic play. Semiotic analysis of internet memes has shown that the combination of verbal and visual signs is typical for the media segment of environmental internet communication.

Unified Transformer With Isomorphic Branches for Natural Language Tracking

Natural language tracking aims to localize the target object referred to by a language description using a sequence of bounding boxes in video frames. Compared with traditional visual single object tracking initialized with only a bounding box (BBox), this task introduces high-level semantic information to reduce the ambiguity of BBox and enhance the ability to retrieve the target in a global manner. Thus, it can yield more accurate and robust tracking results. Previous methods usually adopt off-the-shelf grounding and tracking branches to tackle this task, where feature representations are learned in isolation without benefiting each other. The two branches can associate with each other to discover crucial clues since the language description and the template image provide information from different sources. Therefore, we propose a unified transformer method for natural language tracking named TransNLT, which utilizes isomorphic Transformer structures for grounding and tracking branches where collaborative learning is enabled to construct comprehensive features for the target. In addition, we propose a Selective Feature Gathering (SFG) module which can integrate cross-modal global information of the tracking target from the visual template and language description. Through effective interaction of visual and language information, we can achieve better results than tracking with only a single modality. Extensive experiments on three popular natural language tracking benchmarks show our proposed TransNLT outperforms previous state-of-the-art methods.

Bionic visual navigation model for enhanced template matching and loop closing in challenging lighting environments

In recent years, various computational models have been proposed in neuroscience, but only a few have been utilized in robot visual navigation through engineering approaches. Besides, these engineering methods lack environmental adaptability, especially in the aspect of visual place recognition in weak or uncontrollable light fluctuations. To address this issue and enhance the performance of visual template matching and map loop closure detection in challenging lighting environments, this paper proposed a bionic visual navigation model that combines two neural network architectures, the Pulse Coupled Neural Network (PCNN) and Continuous Attractor Neural Network (CANN). In our navigation model, the visual features of the environments are encoded as temporal information by the spiking model and input into the local view cells for visual template matching. Additionally, we utilize the pose cells network, which incorporates the similarity between current and previous templates, along with odometry data, to encode spatial information and store it as an experience map. To validate the effectiveness of the proposed model, we conducted evaluations on datasets collected within our library, campus, and an open-source office dataset. The experimental results reveal that our algorithm increases the F1-score of template matching by approximately 10.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, 35.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, 61.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, and 1.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document} in each dataset, compared to the conventional RatSLAM method. Furthermore, our algorithm generates a more accurate map that closely correlates with the real-world trajectory without compromising on computation time. The results suggest that our bionic visual navigation model is reliable for both standard and extreme lighting conditions.

A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication

In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks.

Abstract 4625: A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis

Abstract The growth in cancer immunotherapy agents requires an understanding of the immune contexture of the tumor microenvironment (TME). Understanding immune contexture requires multiplex staining, imaging, and analysis to obtain multi-marker phenotypes of specific cells and analyze their biodistribution in the TME. Imaging Mass Cytometry™ (IMC) is the method of choice for single-step staining and highplex imaging of FFPE tissues. FFPE tissue is autofluorescent, which limits the utility of immunofluorescence methods. Lung and colorectal tissue (and bone, skin, etc) are highly autofluorescent, and therefore good targets for IMC. However, developments in analysis software for highplex imagery have not kept pace with imaging advances. We present a comprehensive workflow designed specifically for highplex image analysis, covering tissue segmentation, cell segmentation based on IMC DNA images, cellular phenotyping, and spatial analyses. Lung and colorectal tissue sections with a 30-marker IMC panel of structural, tumor, stroma, immune cell, and immune activation markers were imaged (Hyperion+™, Standard BioTools). Highplex image analysis (Phenoplex™, Visiopharm) was performed as a multi-step workflow in a single software package that includes: conversion of IMC images to pyramidal format; easy visualization methods for displaying different marker subsets; a paint-to-train algorithm for tissue segmentation (into tumor, stroma, blood vessels, etc.); deep-learning-based nuclear segmentation pre-trained on IMC DNA channels; cellular phenotyping based on thresholds based on visual assessment of positivity; spatial biodistribution metrics for cell populations; and a flexible set of outputs for downstream analysis. Tissue segmentation was used to divide the tissue into tumor, stromal, and tumor margin regions, and these regions were used to compare the immune contexture through a series of t-SNE images partitioned by spatial region. We demonstrate that a simple analysis workflow can be used for highplex images of different tissue types by users with no programming knowledge. Visualization templates for the marker subsets and the pre-trained IMC nuclear segmentation are reusable. A new tissue segmentation algorithm for each tissue type is required, as are new thresholds for biomarker positivity. Spatial biodistribution metrics, heatmaps and partitioned t-SNE plots were generated for each tissue type with a minimum of work. Highplex IMC imaging of lung and colorectal tumor samples is a simple and effective means of obtaining high-parameter images without interfering autofluorescence. Having a comprehensive workflow for the analysis of this complex data makes obtaining useful results from highplex images more accessible to biologists and immunologists by circumventing the requirement for expert programming for each specific application. Citation Format: Brenna O'Neill, Smriti Kala, Sam Lim, Clinton Hupple, Nina Lane, Rasmus Norre Sorensen, Rasmus A. Lyngby, Alessandro Massaro, Andreas Hussing, Jeppe Thagaard, Johan Dore-Hansen, James Robert Mansfield. A comprehensive guided workflow for highplex imaging, tissue segmentation, and multiplex cellular phenotyping for tumor microenvironment analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4625.

Bionic Path Planning Fusing Episodic Memory Based on RatSLAM.

Inspired by rodents' ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents' behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

A Sorry Excuse for an Apology: Examining People's Mental Representations of an Apologetic Face.

The online version contains supplementary material available at 10.1007/s10919-022-00422-5.

Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.

Joint visual template and natural language for robust visual tracking

At present, the target of interest in visual tracking is given in the form of a bounding box. Due to the randomness of the target shape, the bounding box may contain a lot of non-target information. When encountering complex tracking scenarios, the performance of the tracker reduces severely. To address this problem, in this letter, the authors propose a novel tracking framework based on the joint of the visual template and natural language (VNTrack) to alleviate the impact of bounding box ambiguity. Specifically, the authors first use a pre-trained language model to extract the features of the language description of the target. Then, a feature alignment module is designed to align and enhance the visual template feature and natural language feature. In addition, the authors design a multimodal query module to fuse the visual template, natural language, and search region information. Experimental results over tracking benchmarks with language annotations show that the proposed VNTrack is competitive among the state-of-the-art trackers.

Internet Memes of 2019—2021 as Current Phenomenon of Internet Communication: Meanings of Unity and Disunity

The events of 2019—2021 affected the social optimism index and the social well-being of Russians, strengthening the constantly present multidirectional trends towards consolidation and isolation in society. The object of the study is borrowed and appeared in the Runet functioning in the specified period Internet memes that translate the meanings of unity and isolation. The relevance of the research is determined by the need to study new principles of information dissemination and its desirable characteristics in an information-overloaded society, the cognitive potential of the “visual turn” in the humanities, the social significance of the content represented by the studied units, the lack of linguistic, interdisciplinary theoretical and empirical studies in this field. The source of the material is a variety of services and libraries of memes on the Internet. The article describes the current state of Internet meme research in the Russian and European humanities. The article presents the analysis of Internet memes selected in accordance with the given semantic characteristics in their dynamics within the framework of N. A. Zinovieva’s typology, built on six dichotomies (truth — lie, character — event, tradition — innovation, background — figure, image — text, thought — action). Observations on the specifics of the precedent phenomena in the formation and development of Internet memes are presented. The structure of the Internet meme is described. It has a field organization, including a constant core formed by a visual template and the generalized meaning of the Internet meme. The conclusion is made about the relevant features of Internet memes of the ideographic group under study, which determine the features of meme encoding of socially significant information and the translation of socio-cultural meanings.

Интернет-мем как актуальный формат политической коммуникации Германии (на материале тематической группы «Олаф Шольц»)

The article presents the study of the semantic and functional characteristics of political Internet memes of the thematic group "Olaf Scholz" in 2020–2022 in Germany. The main parameters for characteristics of memes include place and date, types of addressing, origin and initiator, object and subject of ironic-critical interpretation, the level of inclusion in culture, replicated components, functioning specifics of the precedent phenomenon, interaction paths between visual and verbal, patterns of meaning generation, means of creating comic effect, resulting impact, field structure, genre varieties. Most memes about O. Scholz spontaneously originated within the German linguaculture, reflect the reaction to a specific event, and are addressed to a part of society that is informed about the current national political agenda. The object of criticism unite both characteristics of a politician and his actions. The main subjects of criticism include incompetence, bad reputation, denial of the obvious, insufficient attention to national interests, low performance. Memes appeal mainly to actual precedent phenomena. The background of the precedent phenomenon rarely plays a significant role in generating meaning. The visual component is mostly subject to replication. The visual arrangement imminently contains the image of O. Scholz, optionally bearing symbols of certain actions / events / values, other people with stereotyped relationships, spatial objects as signs of properties attributed to the leader. Consonance-and-complementary relations prevail in coordination among visual and verbal representations. The main sources of the comic effect embody dissonance of the verbal and visual components, transfer of the case of the everyday sphere to the political one; visual accentuation of the elements of the verbal component; identification of the object of criticism with a comic character. Relevant genre varieties of memes about O. Scholz are macros, advice and demotivational memes, and IM parodies. A meme has a field organization, including a constant core formed by a visual template and the generalized meaning.

Ingroup and outgroup differences in face detection.

Humans show improved recognition for faces from their own social group relative to faces from another social group. Yet before faces can be recognized, they must first be detected in the visual field. Here, we tested whether humans also show an ingroup bias at the earliest stage of face processing - the point at which the presence of a face is first detected. To this end, we measured viewers' ability to detect ingroup (Black and White) and outgroup faces (Asian, Black, and White) in everyday scenes. Ingroup faces were detected with greater speed and accuracy relative to outgroup faces (Experiment 1). Removing face hue impaired detection generally, but the ingroup detection advantage was undiminished (Experiment 2). This same pattern was replicated by a detection algorithm using face templates derived from human data (Experiment 3). These findings demonstrate that the established ingroup bias in face processing can extend to the early process of detection. This effect is 'colour blind', in the sense that group membership effects are independent of general effects of image hue. Moreover, it can be captured by tuning visual templates to reflect the statistics of observers' social experience. We conclude that group bias in face detection is both a visual and a social phenomenon.

Beyond Evading Lockout and the ‘Pioneer’s Curse’

Overview: Current research suggests that timing the entry of a technological innovation is a crucial decision for companies. Several issues can complicate this decision, including selecting appropriate performance measures for decision-making and evaluation; applying a dynamic, not static, view on entry timing decisions; understanding how factors from theory inform actual entry timing decisions; and dealing with situations with multiple, opposing entry timing factors. This study used a visual template to collect data from 26 recent successful cases of entry to examine how practitioners evaluate and manage entry timing decisions effectively. Results showed that practitioners evaluate and manage entry timing in more nuanced ways than suggested in the existing literature. Practitioners benefit from a detailed understanding of the dynamic relationship between entry timing factors and determinants of profitability. The study yielded six strategic guiding principles practitioners can use to optimize their entry timing decisions.

Internet Memes as a Current Phenomenon of Political Internet Communication in Germany: Grand Coalition Thematic Group

The object of the research is Internet memes that constitute the thematic group “Grand Coalition” and appeared during the reign of the parliament of the Federal Republic of Germany 2018–2021. Internet memes selected under the given denotative and temporal features are analyzed in dynamics within N. A. Zinoviev’s typology, based on a set of dichotomies (“truth — lie”, “character — event”, “tradition — innovation”, “background — figure”, “image — text”, “thought — action”). A conclusion is made about the field organization of the Internet meme structure. This structure includes the remaining constant kernel formed by the visual template and the generalized meaning of the Internet meme. It is shown that the specificity of the functioning of the precedent phenomenon as part of Internet memes lies in its transformation through the distribution or replacement of elements and subsequent reinterpretation. The very development of the Internet meme, its successful promotion online supposes the decontextualization of the primary meme and its further recontextualization in new communicative situations. The precedent phenomena involved are most often the conductors of negative meanings. The following signs of the grand coalition are regularly reflected in Internet memes — incapacity, mistrust on the part of citizens, and a bad reputation of its members.

An Environmental-Adaptability-Improved RatSLAM Method Based on a Biological Vision Model

Inspired by rodents’ free navigation through a specific space, RatSLAM mimics the function of the rat hippocampus to establish an environmental model within which the agent localizes itself. However, RatSLAM suffers from the deficiencies of erroneous loop-closure detection, low reliability on the experience map, and weak adaptability to environmental changes, such as lighting variation. To enhance environmental adaptability, this paper proposes an improved algorithm based on the HSI (hue, saturation, intensity) color space, which is superior in handling the characteristics of image brightness and saturation from the perspective of a biological visual model. The proposed algorithm first converts the raw image data from the RGB (red, green, blue) space into the HSI color space using a geometry derivation method. Then, a homomorphic filter is adopted to act on the I (intensity) channel and weaken the influence of the light intensity. Finally, guided filtering is used to process the S (saturation) channel and improve the significance of image details. The experimental results reveal that the improved RatSLAM model is superior to the original method in terms of the accuracy of visual template matching and robustness.