In this paper, we propose a multi-agent reinforcement learning approach, POCA-Mix, to achieve collaborative multi-target search with a visual drone swarm. The proposed approach leverages the benefits of curriculum learning and mixed credit assignment to guide the drone swarm in per-forming the search task with only local visual perception in a constrained 3D environment. To validate the performance of the proposed approach, we conducted simulation experiments with various combinations regarding the number of drones and targets. The results demonstrate that the proposed approach outperforms other baseline methods such as PPO and MA-POCA with a higher success rate in different scenarios.
We study Federated Learning (FL) as the promising technology to design the computer vision Machine Learning-based applications, which are robust to possible Data Quality (DQ) degradation in real Intelligent Transportation Systems (ITSs). As ITSs are composed of diverse data generation and communication units, DQ of images they produce and use may vary dramatically. We study the images of various quality distributed over the local clients and perform iterative FL training with two distinct aggregation strategies: Federated Averaging (FedAvg) and Geometric Median (GM). We consequently evaluate the models cross-trained over a single DQ cohort against other DQ categories. Then we analyze the image (traffic signs) recognition performance and its robustness toward the DQ degradation for each training cohort and aggregation strategy. Based on the results, we provide our recommendations on how to train more robust FL-based computer vision applications for ITSs.
Music generation has attracted growing interest with the advancement of deep generative models. However, generating music conditioned on textual descriptions, known as text-to-music, remains challenging due to the complexity of musical structures and high sampling rate requirements. Despite the task’s significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization ability. This paper introduces JEN-1, a universal high-fidelity model for text-to-music generation. JEN-1 is a diffusion model incorporating both autoregressive and non-autoregressive training in an end-to-end manner, enabling up to 48kHz high-fidelity stereo music generation. Through multi-task in-context learning, JEN-1 performs various generation tasks including text-guided music generation, music inpainting, and continuation. Evaluations demonstrate JEN-1’s superior performance over state-of-the-art methods in text-music alignment and music quality while maintaining computational efficiency. Our demo pages are available at https://jenmusic.ai/audio-demos
Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.
Advanced machine learning techniques are very powerful in predictive tasks. However, they are mostly weak in explaining the inference process and they are mostly treated as black-box models. Fuzzy Network (FN) is powerful white-box technique which is capable of dealing with complexity and linguistic uncertainty. In this paper, a method is introduced to optimise Rule Based Networks using Fuzzy C-Means (FCM) for rule reduction, Genetic Algorithms to tune the membership functions and Backward Selection to reduce the inputs and network branches. A case study in transport and telecommuting is used to illustrate the performance of the proposed method. The results show the FN ability to explain the internal process of decision making and its capabilities in transparency and interpretability as an Explainable AI method.
