We present CoStage, an AI-supported embodied co-creation platform that combines LLM-based story generation with multi-robot stage performance to support children’s narrative construction and spatial imagination. Unlike prior AI storytelling tools that are largely screen-based, CoStage allows children to direct robot actors on a 360° tangible stage, transforming written stories into spatially enacted performances. Informed by formative work with domain experts, we evaluated CoStage in a within-subjects study (N = 24) comparing a robot enactment condition with a screen-based animation condition. Our findings indicate that robot-stage performance can heighten immersion, strengthen children’s sense of directorial agency, support spatial sensemaking, and foster a planning–enactment coordination loop during storytelling. This work advances child–computer interaction by offering design implications and demonstrating how embodied AI co-creation can support children’s spatial cognition and narrative engagement.

We present RoboTheater, a multi-robot storytelling platform, designed to explore and demonstrate the expressive capabilities of robots in stage execution. RoboTheater bridges computational narrative generation with embodied robotic execution, enabling robots to perform with varied dialogues and movements to create immersive stage narratives. The system uses large language models (LLMs) to generate structured scripts that are mapped into robots’ movements, speech, and visual projections, with all commands sent over a wireless network. A user study with 13 participants was conducted to evaluate the system’s usability and gather feedback. Our findings indicate that the RoboTheater effectively conveys emotion and character through multimodal cues, sustaining audience engagement and immersion. The work expands multi-robot storytelling and paves the way for creative, narrative applications.

With the accelerating growth of the digital economy, data has emerged as a core asset, making secure and private data trading a pressing necessity. However, traditional centralized data trading platforms face critical challenges, including identity exposure, data leakage, unclear ownership, and lack of trust. Although decentralized, blockchain-based solutions have been proposed, they typically protect only subsets of these properties and seldom provide a unified, verifiable privacy architecture over the entire trading lifecycle. This article introduces a novel decentralized data trading system that comprehensively integrates Groth16-based zero-knowledge proofs (ZKPs), Merkle tree–based data ownership commitments, and smart contracts on blockchain. The proposed system ensures identity anonymity, data confidentiality, ownership traceability, and behavioral privacy while supporting regulatory auditability. Rather than proposing new cryptographic primitives, we reformulate data trading as a zero-knowledge–verifiable privacy problem and embed the resulting privacy logic into the protocol and contract design. The main contributions are as follows. (1) Developing a unified zero-knowledge privacy layer that combines Groth16-based ZKPs with proxy re-encryption, allowing participants to prove transaction eligibility without disclosing identity attributes while keeping traded data encrypted end-to-end. (2) Constructing a zero-knowledge-based ownership lifecycle in which Merkle trees are repurposed as privacy-preserving ownership commitment structures that support unlinkable ownership proof, secure ownership transfer, and privacy-preserving traceability. (3) Designing a malleability-aware ZKP execution framework for Groth16 proofs, implemented via dedicated “anti-malleability” contracts that bind proofs to ownership states, fresh randomness, and protocol stages, thereby mitigating proof malleability and unsafe reuse across the registration–sale–transfer lifecycle. (4) Integrating a trusted regulatory authority into the architecture to enable compliant yet anonymous audits and formulate a system-wide privacy framework covering identity, data, ownership, behavioral, and audit dimensions. Experimental results demonstrate that the system achieves strong privacy guarantees and low on-chain overhead, offering a more robust and privacy-centric approach to data transactions than existing solutions.

Although virtual production (VP) offers cinematic and immersive storytelling by aligning a high-end camera with multiple LED screens through a central server, the creation of high-quality 3D scenery with real-time interactions remains complex and resource-intensive. This paper explores the potential of expanding cinematic virtual production scenes through three innovative AI-driven approaches: (1) AI-generated 360° panoramas from text prompts to produce immersive backgrounds; (2) Direct text/AI-generated image-to-3D environment conversion using mesh generation; (3) An end-to-end AI pipeline for rapid stylized scene construction. We demonstrate each workflow through real-time avatar-interactive shooting scenarios. Our approach bridges technical and artistic domains and aims to show how these AI-driven workflows could accelerate scene creation, enable novel cinematic experiences, and reveal the future potential of visual content generation.

BACKGROUND: Cancer's complexity and heterogeneity pose significant challenges for personalized treatment. Accurate classification of patients into molecular subtypes is critical for targeted therapy and improved outcomes. However, existing methods often fail to simultaneously capture inter-patient heterogeneity and shared molecular patterns in driver gene profiles. RESULTS: To address this limitation, we propose DriverSub-SVM, a novel framework for interpretable cancer subtype classification that integrates patient-specific and cohort-wide driver gene information. Our method first models the bidirectional influence between mutated and dysregulated genes via a random walk on a functional interaction network. It then applies Bayesian Personalized Ranking (BPR) to infer personalized driver gene rankings for each patient. These rankings are aggregated into a consensus driver gene set using the Condorcet. Subsequently, a One-Against-One Multiclass Support Vector Machine (OAO-MSVM) classifies patients based on their gene-level profiles. Evaluated on multiple TCGA datasets, DriverSub-SVM outperformed four state-of-the-art methods, achieving higher accuracy and identifying clinically relevant genes associated with prognosis and therapeutic response. CONCLUSION: DriverSub-SVM offers an effective and interpretable approach for cancer subtype classification by bridging individual heterogeneity and population-level patterns. It enhances understanding of tumor biology and holds promise for precision oncology and biomarker discovery. The source code is available at https://github.com/sjunrong/DriverSub-SVM .

ABSTRACT The discrete Ricci flow is a powerful tool for designing Riemannian metrics on surfaces by user‐defined Gaussian curvatures, which has been widely applied in computer graphics, medical image processing, and computer vision. The discrete Ricci flow algorithm performs surface parameterization by dynamically adjusting edge lengths to conform to a prescribed target curvature. During execution, it requires frequent updates of mesh edge lengths, repeated construction of Hessian matrices, and solving systems of equations, resulting in substantial computational overhead. Classical methods typically employ half‐edge data structures to traverse mesh edges, vertices, and faces for geometric computations. However, as model scale increases, their computational complexity grows exponentially, making large‐scale model processing impractical within acceptable timeframes. To address this computational bottleneck, this paper proposes a GPU‐accelerated Ricci flow computation framework. By reformulating the original iterative process into parallelizable matrix operations, this framework leverages GPU hardware advantages in parallel computing, significantly improving algorithm efficiency. Experimental results demonstrate that the GPU implementation reduces computation time substantially compared to traditional methods, with acceleration effects becoming more pronounced as model resolution increases. This approach not only provides a viable pathway for handling high‐resolution mesh parameterization but also offers new insights for real‐time geometric processing and interactive applications.

Star Pilgrim is an experimental short video artwork displayed on a 16:9 screen. It combines Unreal Engine filmmaking with AIGC techniques. The project used Midjourney to generate a customized dataset of stylized images, which was then used as LoRA training data for style transfer applied to the video footage. Additionally, the creative process involved human-AI collaborative generation of original music and poetry. By blending cinematic visuals with AI-powered generative content, this work aims to explore the unique artistic potential of human and machine creativity. Through its experimental approach, Star Pilgrim seeks to push the boundaries of conventional filmmaking, offering a dreamlike and hyper-real interpretation of the concept.

This paper explores the concept of expanded cinema and its relationship to extended reality (XR), focusing on the potential of artificial intelligence (AI) to expand and extend expressive possibilities. Expanded cinema refers to experimental film and multimedia art forms that challenge the conventions of traditional cinema by creating immersive and interactive experiences for audiences. XR, on the other hand, blurs the line between physical and virtual reality, offering immersive storytelling experiences. Both expanded cinema and XR aim to push the boundaries of traditional norms and create immersive experiences through the integration of technology, interactivity, and cross-sensory elements. The paper emphasizes the role of AI in optimizing 3D scene creation for XR and enhancing the overall experience through a case study. It also presents several AI-based techniques, such as generative models and AI-assisted rendering, that facilitate efficient and effective 3D content creation. Additionally, it explores the use of AI plugins in 3D modeling software and the generation of 3D models and textures from 2D images using techniques like GANs and VAEs. The incorporation of AI to extend and expand opens up new possibilities for immersive experiences in the future.

Nowadays, the heterogeneous characteristics of cancer patients throw a big challenge to precision medicine and targeted therapy. Identifying cancer subtypes shed new light on effective personalized cancer medicine, future therapeutic strategies and minimizing treatment-related costs. Recently, there are many clustering methods have been proposed in categorizing cancer patients. Although these methods obtained a certain achievement in cancer subtype identification, they still fail to fully use the prior known biological information in the model designing process to improve precision and efficiency. It is acknowledged that the driver gene always regulates its downstream genes in the network to perform a certain function. By analyzing the known clinic cancer subtype data, we found some special co-pathways between the driver genes and the downstream genes in the cancer patients of the same subgroup. Hence, we proposed a novel model named DDCMNMF(Driver and Downstream gene Co-Module Assisted Multiple Non-negative Matrix Factorization model) that first for cancer subtypes by identifying co-modules of driver genes and downstream genes. We applied our model on lung and breast cancer datasets and compared it with the other four state-of-the-art models. The final results show that our model could identify the cancer subtypes with high compactness and separateness and achieve a high degree of consistency with the known cancer subtypes. The survival time analysis further proves the significant clinical characteristic of identified cancer subgroups by our model. Availability and implementation: It is available at https://github.com/weiba/DDCMNMF/.

Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.

BACKGROUND: Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. RESULTS: Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. CONCLUSIONS: The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. AVAILABILITY: The source code can be obtained at https://github.com/weiba/Subdyquency .