Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts

Diffusion models have emerged as mainstream framework in visual generation. Building upon this success, the integration of Mixture of Experts (MoE) methods has shown promise in enhancing model scalability and performance. In this paper, we introduce Race-DiT, a novel MoE model for diffusion transformers with a flexible routing strategy, Expert Race. By allowing tokens and experts to compete together and select the top candidates, the model learns to dynamically assign experts to critical tokens. Additionally, we propose per-layer regularization to address challenges in shallow layer learning, and router similarity loss to prevent mode collapse, ensuring better expert utilization. Extensive experiments on ImageNet validate the effectiveness of our approach, showcasing significant performance gains while promising scaling properties.

ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation

This paper presents ArticuBot, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. This task has long been challenging for robotics due to the large variations in the geometry, size, and articulation types of such objects. Our system, Articubot, consists of three parts: generating a large number of demonstrations in physics-based simulation, distilling all generated demonstrations into a point cloud-based neural policy via imitation learning, and performing zero-shot sim2real transfer to real robotics systems. Utilizing sampling-based grasping and motion planning, our demonstration generalization pipeline is fast and effective, generating a total of 42.3k demonstrations over 322 training articulated objects. For policy learning, we propose a novel hierarchical policy representation, in which the high-level policy learns the sub-goal for the end-effector, and the low-level policy learns how to move the end-effector conditioned on the predicted goal. We demonstrate that this hierarchical approach achieves much better object-level generalization compared to the non-hierarchical version. We further propose a novel weighted displacement model for the high-level policy that grounds the prediction into the existing 3D structure of the scene, outperforming alternative policy representations. We show that our learned policy can zero-shot transfer to three different real robot settings: a fixed table-top Franka arm across two different labs, and an X-Arm on a mobile base, opening multiple unseen articulated objects across two labs, real lounges, and kitchens. Videos and code can be found on our project website: https://articubot.github.io/.

FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments

In this paper, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed for further improving the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume.

Skewed Memorization in Large Language Models: Quantification and Decomposition

Memorization in Large Language Models (LLMs) poses privacy and security risks, as models may unintentionally reproduce sensitive or copyrighted data. Existing analyses focus on average-case scenarios, often neglecting the highly skewed distribution of memorization. This paper examines memorization in LLM supervised fine-tuning (SFT), exploring its relationships with training duration, dataset size, and inter-sample similarity. By analyzing memorization probabilities over sequence lengths, we link this skewness to the token generation process, offering insights for estimating memorization and comparing it to established metrics. Through theoretical analysis and empirical evaluation, we provide a comprehensive understanding of memorization behaviors and propose strategies to detect and mitigate risks, contributing to more privacy-preserving LLMs.

Unsupervised Prior Learning: Discovering Categorical Pose Priors from Videos

A prior represents a set of beliefs or assumptions about a system, aiding inference and decision-making. In this work, we introduce the challenge of unsupervised prior learning in pose estimation, where AI models learn pose priors of animate objects from videos in a self-supervised manner. These videos present objects performing various actions, providing crucial information about their keypoints and connectivity. While priors are effective in pose estimation, acquiring them can be difficult. We propose a novel method, named Pose Prior Learner (PPL), to learn general pose priors applicable to any object category. PPL uses a hierarchical memory to store compositional parts of prototypical poses, from which we distill a general pose prior. This prior enhances pose estimation accuracy through template transformation and image reconstruction. PPL learns meaningful pose priors without any additional human annotations or interventions, outperforming competitive baselines on both human and animal pose estimation datasets. Notably, our experimental results reveal the effectiveness of PPL using learnt priors for pose estimation on occluded images. Through iterative inference, PPL leverages priors to refine estimated poses, regressing them to any prototypical poses stored in memory. Our code, model, and data will be publicly available.

HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations

In digital healthcare, large language models (LLMs) have primarily been utilized to enhance question-answering capabilities and improve patient interactions. However, effective patient care necessitates LLM chains that can actively gather information by posing relevant questions. This paper presents HealthQ, a novel framework designed to evaluate the questioning capabilities of LLM healthcare chains. We implemented several LLM chains, including Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and reflective chains, and introduced an LLM judge to assess the relevance and informativeness of the generated questions. To validate HealthQ, we employed traditional Natural Language Processing (NLP) metrics such as Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Named Entity Recognition (NER)-based set comparison, and constructed two custom datasets from public medical note datasets, ChatDoctor and MTS-Dialog. Our contributions are threefold: we provide the first comprehensive study on the questioning capabilities of LLMs in healthcare conversations, develop a novel dataset generation pipeline, and propose a detailed evaluation methodology.

Unlocking Potential in Pre-Trained Music Language Models for Versatile Multi-Track Music Arrangement

Large language models have shown significant capabilities across various domains, including symbolic music generation. However, leveraging these pre-trained models for controllable music arrangement tasks, each requiring different forms of musical information as control, remains a novel challenge. In this paper, we propose a unified sequence-to-sequence framework that enables the fine-tuning of a symbolic music language model for multiple multi-track arrangement tasks, including band arrangement, piano reduction, drum arrangement, and voice separation. Our experiments demonstrate that the proposed approach consistently achieves higher musical quality compared to task-specific baselines across all four tasks. Furthermore, through additional experiments on probing analysis, we show the pre-training phase equips the model with essential knowledge to understand musical conditions, which is hard to acquired solely through task-specific fine-tuning.

Foundation Models for Music: A Survey

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.

Fast and Communication-Efficient Multi-UAV Exploration Via Voronoi Partition on Dynamic Topological Graph

Efficient data transmission and reasonable task allocation are important to improve multi-robot exploration efficiency. However, most communication data types typically contain redundant information and thus require massive communication volume. Moreover, exploration-oriented task allocation is far from trivial and becomes even more challenging for resource-limited unmanned aerial vehicles (UAVs). In this paper, we propose a fast and communication-efficient multi-UAV exploration method for exploring large environments. We first design a multi-robot dynamic topological graph (MR-DTG) consisting of nodes representing the explored and exploring regions and edges connecting nodes. Supported by MR-DTG, our method achieves efficient communication by only transferring the necessary information required by exploration planning. To further improve the exploration efficiency, a hierarchical multi-UAV exploration method is devised using MR-DTG. Specifically, the \emph{graph Voronoi partition} is used to allocate MR-DTG's nodes to the closest UAVs, considering the actual motion cost, thus achieving reasonable task allocation. To our knowledge, this is the first work to address multi-UAV exploration using \emph{graph Voronoi partition}. The proposed method is compared with a state-of-the-art method in simulations. The results show that the proposed method is able to reduce the exploration time and communication volume by up to 38.3\% and 95.5\%, respectively. Finally, the effectiveness of our method is validated in the real-world experiment with 6 UAVs. We will release the source code to benefit the community.

ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets

While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identification studies rely on exhaustive analysis of numerous deep learning features, confining to ad-hoc explainability towards clinicians decision making. In this work, we delve into explainability of ECG re-identification risks using transparent machine learning models. We use SHapley Additive exPlanations (SHAP) analysis to identify and explain the key features contributing to re-identification risks. We conduct an empirical analysis of identity re-identification risks using ECG data from five diverse real-world datasets, encompassing 223 participants. By employing transparent machine learning models, we reveal the diversity among different ECG features in contributing towards re-identification of individuals with an accuracy of 0.76 for gender, 0.67 for age group, and 0.82 for participant ID re-identification. Our approach provides valuable insights for clinical experts and guides the development of effective privacy-preserving mechanisms. Further, our findings emphasize the necessity for robust privacy measures in real-world health applications and offer detailed, actionable insights for enhancing data anonymization techniques.