Space Representation Research Articles

Representations of the everyday life in Nairobi city in Kenyan film

ABSTRACT Representations of urban spaces and most importantly, of city culture, is often manifested in and visibilized through popular cultural productions. City culture is embodied in popular artforms such as film, photography, dance, music, and fashion among others, which present the city as an imagined space for performance and display. Whereas the city is integral in understanding manifestations of social change, film plays a role in foregrounding and reflecting its social transformations overtime. Anchored on the multi-disciplinary triad of the cultural production of place, space and meaning, this article explores representations of everyday life in Nairobi city as mediated through three films: Ndoto za Elibidi (2010), Nairobi Half-life (2012) and Rafiki (2018). The article argues that the production and consumption of urban cinematic geographies is, in different ways, tied to everyday experiences in the city. The article further shows how space in film is used as a narrative devise to depict emerging queer social imaginaries in the city spaces in Nairobi. Thus, the article contributes to emerging trends on how urban practices influence popular consciousness and adds to the growing lore of urban scholarship in Kenya.

Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Transformations are a key tool in the qualitative study of dynamical systems: transformations to a normal form, for example, underpin the study of instabilities and bifurcations. In this work, we test, and when possible establish, an equivalence between two different artificial neural networks by attempting to construct a data-driven transformation between them, using diffusion maps with a Mahalanobis-like metric. If the construction succeeds, the two networks can be thought of as belonging to the same equivalence class. We first discuss transformation functions between only the outputs of the two networks; we then also consider transformations that take into account outputs (activations) of a number of internal neurons from each network. Whitney's theorem dictates the number of (generic) measurements from one of the networks required to reconstruct each and every feature of the second network. The construction of the transformation function relies on a consistent, intrinsic representation of the network input space. We illustrate our algorithm by matching neural network pairs trained to learn (a) observations of scalar functions, (b) observations of two-dimensional vector fields, and (c) representations of images of a moving three-dimensional object (a rotating horse). We also demonstrate reconstruction of a network's input (and output) from minimal partial observations of intermediate neuron activations. The construction of equivalences across different network instantiations clearly relates to transfer learning and will also be valuable in establishing equivalence between different machine learning-based tools.

Rethinking general time series analysis from a frequency domain perspective

Recently, Transformers and MLPs based models have dominated and made significant progress in time series analysis. However, these methods struggle to capture the complete periodic features to model the global dependencies of time series. To this end, we provide a new perspective to explore the potential of time series in the frequency domain. Specifically, we propose the DFT-based Amplitude-Phase Decoupling Network (APDNet), which learns the significance changes and periodicity characteristics of time series by decoupling the real and imaginary parts in the complex frequency domain. First, we propose the Fourier Temporal/Variable Interaction Module to model time dependency and dependency multiple variables, respectively. Secondly, we innovatively propose the Dimensional Expanded Feature Embedding Module (DEFEM), which expands time series into a higher-dimensional representation space to preserve more independent features. Finally, through extensive experiments on multiple real-world datasets, our APDNet achieves state-of-the-art performance in various tasks such as long-term and short-term forecasting, anomaly detection, imputation, and classification. Code is available at: https://github.com/CodeJester196/APDNet.

M-XAF: Medical explainable diagnosis system of atrial fibrillation based on medical knowledge and semantic representation fusion

BackgroundElectrocardiogram (ECG) is a critical diagnostic tool used for screening atrial fibrillation (AF). In recent years, several deep learning-based ECG automatic diagnosis methods have been proposed and have yielded satisfactory results. However, the lack of interpretability in these systems limits their clinical usage. MethodsTo address this issue, we propose a medical explainable AF diagnosis system (M-XAF) that not only predicts AF diagnostic results but also generates an analysis report of the ECG medical features that influenced the classification model's decision. Using a novel ECG signal features occlusion analysis method, M-XAF establishes a connection between the medical knowledge space and the representation space from the convolutional neural network (CNN) by extracting understandable semantic sensitive channels (SSCs) via gradient-weighted channel sensitivity. Through the visualization and statistical modeling of SSCs, M-XAF provides multimodal explanations, including ECG feature descriptions, regions of interest (ROI), and class activation mapping (CAM), for AF diagnostic results. ConclusionThe experimental results demonstrate that the M-XAF has achieved satisfactory results in the AF identification task, with accuracy, specificity, PPV, F1, and MCC reaching over 0.9715, 0.9896, 0.9778, 0.9567, and 0.9365 on two public ECG datasets. In addition, M-XAF generates quantitative explainable reports and CAM for each predicted sample from the perspective of classification models. Furthermore, the explainable reports provided by M-XAF can address the high-confidence problem in classification models. Ultimately, the explainable framework in M-XAF can be widely applied in AI-ECG classification scenarios.

Semi-supervised prototype network based on compact-uniform-sparse representation for rotating machinery few-shot class incremental fault diagnosis

Distinct from the ideal ‘closed-set’ assumption of comprehensive fault types and arbitrary data access, the entangled challenges of labeled instance paucity and fault increasing with operation are difficult to circumvent in industrial diagnostics. However, conventional deep learning-based diagnostic models lack the capacity for dynamic and incremental acceptance of new faults under limited labeled instances. When confronted with this more industrially realistic diagnostic task, their performance is constrained by overfitting, imbalance, and catastrophic forgetting, thus diminish the feasibility of engineering deployment. In this research, we define this task as a few-shot class incremental fault diagnosis (FS-CIFD) problem and develop a semi-supervised prototype network based on compact-uniform-sparse representation (CUS-SSPN) for this purpose. Structurally, CUS-SSPN incorporates the designed compact-uniform-sparse (CUS) composite loss, optimal transport (OT)-based semi-supervised strategy, and multi-level knowledge distillation (KD) strategy into the prototype network. With the CUS composite loss and OT-based semi-supervised strategy, CUS-SSPN can assign representative prototypes for each incremental fault and form an intra-class compact and inter-class uniform and sparse representation space. This allows the model to incrementally learn new faults while effectively suppressing the overfitting triggered by instance paucity and the biased diagnosis triggered by unbalanced labeling. Combining the designed multi-level KD strategy to convey category centroid information and classification decision knowledge from both prototype and logits levels, catastrophic forgetting of known faults can be effectively mitigated. Extensive FS-CIFD tasks are constructed on both publicly available dataset and practical wind turbine dataset to validate the effectiveness of CUS-SSPN and the feasibility of engineering diagnostics.

Toward Unbiased High-Quality Portraits through Latent-Space Evaluation.

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent space in exploring complex realities such as image space. We focus on DaVinciFace, an AI-based system that explores the StyleGAN2 space to create a high-quality portrait for anyone in the style of the Renaissance genius Leonardo da Vinci. The user enters one of their portraits and receives the corresponding Da Vinci-style portrait as an output. Since most of Da Vinci's artworks depict young and beautiful women (e.g., "La Belle Ferroniere", "Beatrice de' Benci"), we investigate the ability of DaVinciFace to account for other social categorizations, including gender, race, and age. The experimental results evaluate the effectiveness of our methodology on 1158 portraits acting on the vector representations of the latent space to produce high-quality portraits that retain the facial features of the subject's social categories, and conclude that sparser vectors have a greater effect on these features. To objectively evaluate and quantify our results, we solicited human feedback via a crowd-sourcing campaign. Analysis of the human feedback showed a high tolerance for the loss of important identity features in the resulting portraits when the Da Vinci style is more pronounced, with some exceptions, including Africanized individuals.

Developing a Data Model for an Omnidirectional Image-Based Multi-Scale Representation of Space

Abstract. One of the major challenges that existing spatial data is facing is the fragmentation of its representation of indoor and outdoor space. As studies in the use of omnidirectional images in representing space and providing Location-based Services (LBS) has been increasing, the representation of the different scales of space, both in indoors and outdoors, has yet to be addressed. This study aims to develop a data model for generating a multi-scale image-based representation of space using omnidirectional images based spatial relationships. This paper identifies the different scales of space that are represented in spatial data and extends previous approaches of using omnidirectional images in providing indoor LBS towards representing the other scales of space, particularly in outdoor space. Using a sample data, we present an experimental implementation to demonstrate the potential of the proposed data model. Results show that apart from the realistic visualization that image data provides, basic spatial functions can be performed on the image data constructed based on the proposed data model.

Prediction of the Change Rate of Tumor Cells, Healthy Host Cells, and Effector Immune Cells in a Three-Dimensional Cancer Model using Extended Kalman Filter

In this study, we develop and implement the Extended Kalman Filter (EKF) to forecast the rate of change in tumor cells, healthy host cells, and effector immune cells within the Itik-Banks model. This novel application of EKF in cancer dynamics modeling aims to provide precise real-time estimations of cellular interactions, especially in constructing a new state space representation from the Itik-Banks model. We use a first-order Taylor series to linearize the model. The numerical simulations were performed to analyze the accuracy of this new state space with data from William Gilpin’s GitHub repository. The results show that the EKF predictions strongly align with actual data, i.e., in the prior and posterior steps for tumor and healthy host cells, there is a strong agreement between the predictions and the actual data. The EKF captures the oscillatory nature of the tumor and healthy host cell population well. The peaks and troughs of the predictions align closely with the actual data, indicating the EKF’s effectiveness in modeling the dynamic behavior of the tumor and healthy host cells. However, for effector immune cells, the oscillatory nature of the data in these cells gives rise to slight deviations. This represents a significant challenge in the future for updating the state space representations. Despite minor discrepancies, the EKF demonstrates a strong performance in both the training and testing data, with the posterior step estimates significantly improving the prior step accuracy. This study emphasizes the importance of data availability for accurate predictions, noting a symmetric Mean Absolute Percentage Error (sMAPE) of 35.92% when data is unavailable. Prompt correction with new data is essential to maintain accuracy. This research underscores the EKF’s potential for real-time monitoring and prediction in complex biological systems.

Советская эпоха в поликодовой прецедентной медиарефлексии, или От каждого по способностям, каждому по потребностям

The article studies linguasemiotic features and functional meanings of precedent phenomena (PP) of the Soviet past through the prism of polycode representation in modern discursive space. The study is aimed at analyzing Soviet precedents of graphic design, sociocultural life, everyday life and gluttony in mass media communication. During pinpoint sampling of polycode units (Internet memes) from the Russian-language Internet segment, 500 texts that met the criterion of featuring a verbal or visual PP representative with the semantic component “Soviet, relating to the historical period of the USSR” were identified. The analysis of PP that implement media reflection of the cultural and historical heritage of the Soviet epoch was carried out based on semiotic, structural and semantic, intertextual, stylistic and discourse analyses. The features of updating the associative connection between the artistic and aesthetic design of polycode text and socio-cultural signs of the Soviet period have been established. The polycode nature of precedent references to the cinematic and musical heritage of the Soviet period is confirmed by the presence of both verbalized elements (names, titles) and non-verbal (audio fragments, captions and visual images); the discursive PP actualization is presented in reminiscent and allusive formats. Text examples demonstrate the dominance of a visual precedent image, appealing to gluttony perceptions and mediated by the context of the past or the era of the Soviet Union. Two models of encoding PP related to Soviet everyday life have been established: the retrospective one (reliance on visual representatives of archaism and verbal temporality representatives) and the axiological one (the verbal part of the message postulating the high quality of products produced in the USSR; visual elements act as a referential basis appealing to culturally specific precedent base).

Efficient molecular conformation generation with quantum-inspired algorithm.

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature. The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Novel CNN-based transformer integrating Boruta algorithm for production prediction modeling and energy saving of industrial processes

Industrial process data collected by sensors have characteristics of high dimensionality, non-linearity and dynamics. Consequently, the selection extraction is regarded as a critical part for reducing the dimension of the data and removing irrelevant variables of constructing the production prediction model of industrial processes. Therefore, a novel production prediction model using the Boruta algorithm integrating the convolutional neural network-based Transformer (BCT) is proposed in this paper. Primarily, the Boruta algorithm maps nonlinear high-dimensional data to a low-dimensional space to select features that are meaningful to the yield of the industrial process. Then, the features are extracted adaptively using a convolutional neural network (CNN), which is encoded based on the transformer layer to learn relevant information about the different representation spaces. Furthermore, a linear layer with highway connections is employed to obtain prediction results. Finally, the BCT method is applied to establish a realistic production prediction model of actual liquefied petroleum gas plants for energy saving. Compared with back propagation neural networks, the radial basis function, the extreme learning machine, and the transformer based on the CNN, the BCT method achieves a state-of-the-art level. Furthermore, the BCT method provides the operation guidance on the actual liquefied petroleum gas (LPG) production process with increasing the LPG yield by 17.21%, which can improve production efficiency and reducing energy consumption.

Temporal information in the anterior cingulate cortex relates to accumulated experiences

Anterior cingulate cortex (ACC) activity is important for operations that require the ability to integrate multiple experiences over time, such as rule learning, cognitive flexibility, working memory, and long-term memory recall. To shed light on this, we analyzed neuronal activity while rats repeated the same behaviors during hour-long sessions to investigate how activity changed over time. We recorded neuronal ensembles as rats performed a decision-free operant task with varying reward likelihoods at three different response ports (n= 5). Neuronal state space analysis revealed that each repetition of a behavior was distinct, with more recent behaviors more similar than those further apart in time. ACC activity was dominated by a slow, gradual change in low-dimensional representations of neural state space aligning with the pace of behavior. Temporal progression, or drift, was apparent on the top principal component for every session and was driven by the accumulation of experiences and not an internal clock. Notably, these signals were consistent across subjects, allowing us to accurately predict trial numbers based on a model trained on data from a different animal. We observed that non-continuous ramping firing rates over extended durations (tens of minutes) drove the low-dimensional ensemble representations. 40% of ACC neurons' firing ramped over a range of trial lengths and combinations of shorter duration ramping neurons created ensembles that tracked longer durations. These findings provide valuable insights into how the ACC, at an ensemble level, conveys temporal information by reflecting the accumulation of experiences over extended periods.

Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring

Anomaly detection is indispensable for ensuring the reliable operation of grid-connected photovoltaic (PV) systems. This study introduces a semi-supervised deep learning approach for fault detection in such systems. The method leverages a variational autoencoder (VAE) to extract features and identify anomalies. By training the VAE on normal operation data, a compact latent space representation is created. Abnormal observations, indicating faults, exhibit distinct feature vectors in this latent space. Multiple anomaly detection algorithms, including Isolation Forest, Epileptic Envelope, Local Outlier Factor, and One-Class SVM, are employed to discern normal and abnormal observations. This semi-supervised approach only requires fault-free data for training, without labeled faults, making it attractive in practice. A publicly available dataset, the Grid-connected PV System Faults (GPVS-Faults) dataset, which includes data from a PV plant operating in both maximum power point tracking (MPPT) and intermediate power point tracking (IPPT) switching modes, is used for evaluation. The proposed approach is assessed across various fault scenarios, such as partial shading, inverter faults, and MPPT/IPPT controller faults in boost converters. The outcomes underscore the effectiveness of VAE-based techniques in accurately identifying these faults, with accuracy rates reaching up to 92.90% for MPPT mode and 92.99% for IPPT mode, thus contributing to the robustness of fault detection in grid-connected PV systems.

Social, Religious, and Economic Interaction Spaces as a Community Representational Space

Social, Religious, and Economic Interaction Spaces as a Community Representational Space

Unleashing the potential of cheminformatic analysis for Mycobacterium tuberculosis inhibitors: Insights into chemical space and structural diversity

Mycobacterium tuberculosis (Mtb) remains a major global health concern, causing millions of infections and deaths annually. Drug-resistant tuberculosis poses significant challenges, necessitating the search for new Mtb inhibitors. In this context, a cheminformatic analysis of the currently available antimycobacterial chemical space can be performed to discover structural diversity and new scaffolds. This study conducts a comprehensive cheminformatic analysis of two datasets containing Mtb inhibitors (Mtbs and Phytochemicals), comparing them with Approved drugs and Nutraceuticals datasets. The analysis includes physicochemical properties, molecular scaffolds, structural fingerprints, and structural similarities. The analysis of physicochemical property distributions revealed that Mtb inhibitors are generally less polar or equally polar compared to approved drugs and have similar flexibility. The visual representation of the property space illustrates that the Mtbs dataset is confined to a narrower area, whereas certain compounds within the Phytochemical dataset broaden the scope of the traditional medicinal space. Scaffold analysis identifies several promising candidates in both the Mtbs and Phytochemical datasets. Additionally, employing SkelSpheres descriptors and rubber band scaling for similarity analysis reveals a limited depiction of the chemical space. The analysis of structural diversity indicates that the compounds in the Mtbs dataset exhibit less structural diversity. These findings underscore the necessity to broaden the chemical space of Mtb-targeting molecules by incorporating representative molecules from diverse scaffolds to effectively tackle the issue of drug resistance.

Molecular sharing and molecular-specific representations for multimodal molecular property prediction

Molecular property prediction plays a crucial role in drug discovery and development. However, traditional experimental measurements and Quantitative Structure-Activity Relationship (QSAR) models are often expensive, time-consuming, and data acquisition is challenging. To overcome these limitations and challenges, this study innovatively proposes a fusion molecular property prediction method called molecular property prediction model (MSSP) to address the non-uniqueness of Simplified Molecular Input Line Entry System (SMILES) string representation and the difficulty of capturing global information in molecular graphs. This method extracts multiple fingerprint features and utilizes graph neural network encoding to map different modalities of molecules into molecular sharing and molecular-specific representation spaces, achieving modal alignment and fusion of molecules by combining molecular invariance and representation specificity. To enhance the interpretability and visualization capabilities of the model, graph attention mechanisms are introduced, enabling the identification and inference of important chemical fragments within molecules. Experimental results on publicly available cell line phenotype and kinase activity datasets demonstrate that MSSP outperforms the current state-of-the-art methods in molecular property prediction. Additionally, MSSP exhibits strong competitiveness across nine benchmark molecular property prediction datasets. Furthermore, in the task of predicting SRC kinase data properties, this study successfully screens promising therapeutic compounds from compound libraries by validating the predictions of the MSSP model and combining them with traditional methods such as molecular docking and molecular dynamics simulations. Multiple potential Lyn inhibitors have been discovered through this approach. The application of MSSP model is helpful to discover new molecules with new drug properties or functions, accelerate the process of drug discovery, save time and resources, and provide important guidance for drug discovery.

Generative AI unlocks PET insights: brain amyloid dynamics and quantification.

Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics. Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution. We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression. Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking.

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

“It Might Have a Little to Do with Wish Fulfillment”: The Life-Giving Force of Queer Performance in TTRPG Spaces

In Cruel Optimism, Lauren Berlant asserts that “fantasy [can serve] as a life-sustaining defense against the attritions of ordinary violent history,” which is a statement that holds true for many queer role-players in TTRPG spaces (Berlant 2012, 45). Around the table, these worlds enable players to discover and render their identities legible in social utopian spaces and rehearse their performance for life outside of them. Through engaging with scholarship, autoethnographic writing, and cultural production surrounding TTRPGs, queer identity, avatars, and gender performance, this project asserts that TTRPGs function as safe spaces for becoming, due in part to the ability to create an avatar, or “flexible representational stand-in” for the self, that is queer and embodied through role-play (McMillan, 2015 13). This is achieved through tracking the author’s experience of discovering and forming their queer and trans identity through role-playing various characters and disciplining their function through related interdisciplinary theory across queer, feminist, and role-playing studies. Additionally, through utilizing additional ethnographic accounts and analyzing TTRPG-based media in theatre and actual play spaces, this project further establishes how the emancipatory potential in escapist TTRPG fantasy spaces can ultimately alter queer role-players’ engagement with material reality in a world adversarial to their existence and visibility, especially in light of the past few years.