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Federated Semi-Supervised Learning (FSSL) has emerged as a crucial topic in medical image analysis, allowing multiple medical institutions to collaboratively train a global model using limited labeled data. However, existing FSSL methods focus solely on an effective combination of federated learning and semi-supervised learning, ignoring the heterogeneity of client data and the inadaptability of semi-supervised methods in diverse environments, which leads to knowledge bias in local models and impedes stable convergence. To this end, we explore the application of personalization in FSSL and propose a novel dual-calibrated co-training framework. To adapt to the unique feature distribution of client data, we consider collaborative relationships among clients to aggregate a personalized model for each client. We further build a dual-student architecture with the personalized model and private local model on the client side, which encourages model disagreement for co-training while enhancing participant privacy. Most importantly, we design dual calibration strategies that adaptively optimize the model: Local calibration improves the boundary discrimination of the local model by dynamically replacing pseudo-label boundary patches; Global calibration corrects model direction based on the real-time perception of the biases between local dual-student models. Experimental results show the effectiveness of our method on a private medical dataset and two public medical datasets. The code will be available online.

AAAI 2025

Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



High-quality, pixel-level annotated datasets are crucial for training deep learning models, while their creation is often labor-intensive, time-consuming, and costly. Generative diffusion models have then gained prominence for producing synthetic datasets, yet existing text-to-data methods struggle with generating complex scenes involving multiple objects and intricate spatial arrangements. To address these limitations, we introduce FlexDataset, a framework that pioneers the composition-to-data (C2D) paradigm. FlexDataset generates high-fidelity synthetic datasets with versatile annotations, tailored for tasks like salient object detection, depth estimation, and segmentation. Leveraging a meticulously designed composition-to-image (C2I) framework, it offers precise positional and categorical control. Our Versatile Annotation Generation (VAG) \textit{Plan A} further enhances efficiency by exploiting rich latent representations through tuned perception decoders, reducing annotation time by nearly fivefold. FlexDataset allows unlimited generation of customized, multi-instance and multi-category (MIMC) annotated data. Extensive experiments show that FlexDataset sets a new standard in synthetic dataset generation across multiple datasets and tasks, including zero-shot and long-tail scenarios.

FlexDataset: Crafting Annotated Dataset Generation for Diverse Applications

Querying temporal data has recently gained traction in several artificial intelligence applications. As operational domains of intelligent agents are constantly being expanded, there is a strong need for representing domain knowledge. This comes in the form of ontologies, which are predominantly expressed in description logics and enrich time-stamped data to temporal knowledge bases. For modeling highly complex system environments, expressive description logics are often the formalism of choice. Querying such temporal knowledge bases is a challenging task, but recently a first practical solution has been put forward. We propose a novel approach to the query answering problem based on two well-known rewriting rules from temporal logic. After a careful theoretical analysis of our algorithm, we show in a practical evaluation on several benchmarks that it outperforms state of the art, sometimes by orders of magnitude. Based on our findings, we also propose a fragment of temporal conjunctive queries which guides users towards well-performing queries.

Temporal Conjunctive Query Answering via Rewriting

Neural Radiance Field (NeRF)-based volumetric video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences that provide audiences with unprecedented immersion and interactivity. However, the substantial data volumes pose significant challenges for storage and transmission. Existing solutions typically optimize NeRF representation and compression independently or focus on a single fixed rate-distortion (RD) tradeoff. In this paper, we propose VRVVC, a novel end-to-end joint optimization variable-rate framework for volumetric video compression that achieves variable bitrates using a single model while maintaining superior RD performance. Specifically, VRVVC introduces a compact tri-plane implicit residual representation for inter-frame modeling of long-duration dynamic scenes, effectively reducing temporal redundancy. We further propose a variable-rate residual representation compression scheme that leverages a learnable quantization and a tiny MLP-based entropy model. This approach enables variable bitrates through the utilization of predefined Lagrange multipliers to manage the quantization error of all latent representations. Finally, we present an end-to-end progressive training strategy combined with a multi-rate-distortion loss function to optimize the entire framework. Extensive experiments demonstrate that VRVVC achieves a wide range of variable bitrates within a single model and surpasses the RD performance of existing methods across various datasets.

VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional ”pre-train, fine-tune” framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered
features fail to capture the rich semantic nuances within the text. And the conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGL$_{sta}$), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. And graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced. Through extensive experimentation across various benchmarks, our approach consistently outperforms existing methods, offering promising advancements in the field.

TGLsta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs

We propose a 2D simulation system for multi-agent collective construction (MACC) based on simple line-following intelligent machines (SLIM) - small differential drive mobile robots. Our MACC-SLIM system alleviates the high upfront cost of implementing MACC on real hardware. Our system builds upon widely available resources, namely a standard LCD screen and commodity mobile robots, allowing researchers and schools easier access to MACC hardware implementation. We test the system on plans generated by an optimal state-of-the-art MACC algorithm, demonstrating there are still non-insignificant synchronization delays. The MACC-SLIM system allows us to observe bottlenecks, parallelism, and possible execution failures of plans generated by the MACC algorithms.

Accessible Hardware Implementation for Multi-Agent Collective Construction

The increasing demand for real-time analysis in video streaming has driven significant advancements in object detection and motion prediction. This paper presents SkelAI, an innovative application that combines YOLOv8, OpenCV, OpenAI
API, and our own innovative algorithms to achieve real-time object detection and medial axis skeletonization tailored explicitly for live video streaming environments. In addition, SkelAI integrates AI-generated image capabilities through
the DALL-E 3 model, enabling the extraction of skeletons from synthetic content that simulates streaming scenarios. The application supports exporting skeleton data in PyTorch-compatible formats, facilitating the training of sequence predicting deep learning models. Comprehensive evaluations demonstrate SkelAI’s enhanced accuracy, efficiency, and versatility compared to existing tools, underscoring its potential
applications in digital animation, biomechanical research and robotics, human-computer interaction, and video compression within streaming platforms.

Real-Time Object Detection and Skeletonization for Motion Prediction in Video Streaming

In machine learning applications over Big streaming Data, Neural Networks (NNs) are continuously and rapidly trained over voluminous data arriving at high speeds. As soon as a new version of the NN becomes available, it gets deployed for prediction purposes (e.g. classification). The real-time character of such applications greatly depends on the volume and velocity of the data streams, as well as the NN complexity. Training on large volume of ingested streams or using complex NNs, potentially increases accuracy, but may compromise the real-time character of those applications. In this work, we present SuBiTO, a framework that automatically and continuously learns the training time vs accuracy trade-offs as new data stream in and fine tunes: (i) the number, size and type of NN layers; (ii) the size of the ingested data via stream synopses specific parameters; and (iii) the number of training epochs. Finally, SuBiTO suggests optimal sets of such parameters and detects concept drifts, enabling the human operator adapt these parameters on-the-fly, at runtime.

SuBiTO: Synopsis-based Training Optimization for Continuous Real-Time Neural Learning over Big Streaming Data

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.

RLLTE: Long-Term Evolution Project of Reinforcement Learning

Probabilistic logic shields integrate deep reinforcement learning (RL) with probabilistic logic reasoning to train agents that operate in uncertain environments while giving strong guarantees with respect to logical constraints, such as safety properties. In this demo paper, we introduce a codebase that streamlines the design of custom MiniHack environments where neurosymbolic RL agents leverage probabilistic logic shields to learn safe and interpretable policies with strong guarantees. Our framework allows expert users to easily define and train agents that integrate deep neural policies with probabilistic logic in arbitrarily complex games: from simple exploration to planning and interacting with enemies. Additionally, we provide a web-based platform that showcases our application, offering an interactive interface for the broader community to experiment with and explore the capabilities of neurosymbolic reinforcement learning. This lowers the barrier for researchers and developers, making it accessible for a wider audience to engage with safety-critical RL scenarios.

Neurosymbolic Reinforcement Learning: Playing MiniHack with Probabilistic Logic Shields

We present SPASCA - a conversational AI system that promotes psychological and cognitive well-being of persons living with dementia (PLWD). This system features an AI agent that provides social presence and support to PLWD through verbal communications, without physical presence of human caregivers. The system integrates (1) a novel dialogue model that generates dialogue items relevant to the user's experiences and lifestyle, (2) a digital avatar in the form of a talking head with the identity of a caregiver who is familiar to the demented user. We develop prototypes that adopt various interaction modalities and conversational styles and report the pros and cons of different system configurations through expert review. Our system shows the potential of conversational AI for personalized and affordable healthcare services.

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