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Youling: An AI-assisted lyrics creation system

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arXiv 2022年

作者： Zhang, Rongsheng Mao, Xiaoxi Li, Le Jiang, Lin Chen, Lin Hu, Zhiwei Xi, Yadong Fan, Changjie Huang, Minlie Fuxi AI Lab NetEase Inc. Hangzhou China Department Of Computer Science And Technology Institute For Artifical Intelligence State Key Lab Of Intelligent Technology And Systems Beijing National Research Center For Information Science And Technology Tsinghua University Beijing China

Recently, a variety of neural models have been proposed for lyrics generation. However, most previous work completes the generation process in a single pass with little human intervention. We believe that lyrics creation is a creative process with human intelligence centered. AI should play a role as an assistant in the lyrics creation process, where human interactions are crucial for highquality creation. This paper demonstrates Youling, an AI-assisted lyrics creation system, designed to collaborate with music creators. In the lyrics generation process, Youling supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context. The system also provides a revision module which enables users to revise undesired sentences or words of lyrics repeatedly. Besides, Youling allows users to use multifaceted attributes to control the content and format of generated lyrics. The demo video of the system is available at https://***/DFeNpHk0pm4. Copyright © 2022, The Authors. All rights reserved.

关键词： Music

Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling

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arXiv 2023年

作者： Gebhard, Timothy D. Wildberger, Jonas Dax, Maximilian Angerhausen, Daniel Quanz, Sacha P. Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany ETH Zurich Institute for Particle Physics & Astrophysics Wolfgang-Pauli-Strasse 27 Zurich8093 Switzerland ETH Zurich Department of Earth Sciences Sonneggstrasse 5 Zurich8092 Switzerland ETH Zurich Department of Computer Science Universitätsstrasse 6 Zurich8092 Switzerland

Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computationally expensive, thus sparking an interest in solutions based on machine learning (ML). In this ongoing work, we first explore flow matching posterior estimation (FMPE) as a new ML-based method for AR and find that, in our case, it is more accurate than neural posterior estimation (NPE), but less accurate than nested sampling. We then combine both FMPE and NPE with importance sampling, in which case both methods outperform nested sampling in terms of accuracy and simulation efficiency. Going forward, our analysis suggests that simulation-based inference with likelihood-based importance sampling provides a framework for accurate and efficient AR that may become a valuable tool not only for the analysis of observational data from existing telescopes, but also for the development of new missions and instruments. Copyright © 2023, The Authors. All rights reserved.

关键词： Importance sampling

Distributed formation control of networked mechanical systems

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arXiv 2022年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach. © 2022, CC BY.

关键词： Networked control systems

Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

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Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

International Conference on intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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Regulated Pure Pursuit for Robot Path Tracking

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arXiv 2023年

作者： Macenski, Steve Singh, Shrijit Martín, Francisco Ginés, Jonatan R&D Innovations Samsung Research America Clyde Ave Mountain ViewCA94043 United States Department of Computer Science Manipal Institute of Technology Udupi - Karkala Rd Eshwar Nagar Karnataka Manipal576104 India Intelligent Robotics Lab. Rey Juan Carlos University Camino del Molino Fuenlabrada Madrid28943 Spain

The accelerated deployment of service robots have spawned a number of algorithm variations to better handle real-world conditions. Many local trajectory planning techniques have been deployed on practical robot systems successfully. While most formulations of Dynamic Window Approach and Model Predictive Control can progress along paths and optimize for additional criteria, the use of pure path tracking algorithms is still commonplace. Decades later, Pure Pursuit and its variants continues to be one of the most commonly utilized classes of local trajectory planners. However, few Pure Pursuit variants have been proposed with schema for variable linear velocities - they either assume a constant velocity or fails to address the point at all. This paper presents a variant of Pure Pursuit designed with additional heuristics to regulate linear velocities, built atop the existing Adaptive variant. The Regulated Pure Pursuit algorithm makes incremental improvements on state of the art by adjusting linear velocities with particular focus on safety in constrained and partially observable spaces commonly negotiated by deployed robots. We present experiments with the Regulated Pure Pursuit algorithm on industrial-grade service robots. We also provide a high-quality reference implementation that is freely included ROS 2 Nav2 framework at https://***/ros-planning/navigation2 for fast evaluation. © 2023, CC BY.

关键词： Motion planning

FEDCSD: A FEDERATED LEARNING BASED APPROACH FOR CODE-SMELL DETECTION

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arXiv 2023年

作者： Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed Kebande, Victor Awaysheh, Feras M. Palomba, Fabio Awad, Mohammed Center for Applied Intelligent Systems Research School of Information Technology Halmstad University Halmstad Sweden Department of Software Engineering Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Jordan Internet of Things and People Research Center Malmö University Sweden Department of Computer Science Blekinge Institute of Technology Sweden Institute of Computer Science Delta Research Centre University of Tartu Estonia SeSa Lab University of Salerno Italy Arab American University Palestine Palestine

Software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： computer software selection and evaluation

Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

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arXiv 2023年

作者： Yang, Peng Zhang, Laoming Liu, Haifeng Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen518055 China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen518052 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Safe Value Functions

arXiv 2021年

作者： Massiani, Pierre-François Heim, Steve Solowjow, Friedrich Trimpe, Sebastian The Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany The Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart70569 Germany The Biomimetic Robotics Lab Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge02139 United States

Safety constraints and optimality are important but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to maintain formal guarantees, it is also common practice in reinforcement learning to simply modify reward functions by penalizing failures, with the penalty treated as a mere heuristic. We rigorously examine the relationship of both safety and optimality to penalties, and formalize sufficient conditions for safe value functions (SVFs): value functions that are both optimal for a given task, and enforce safety constraints. We reveal this structure by examining when rewards preserve viability under optimal control, and show that there always exists a finite penalty that induces a safe value function. This penalty is not unique, but upper-unbounded: larger penalties do not harm optimality. Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact. This insight suggests practical, theory-guided heuristics to design reward functions for control problems where safety is important. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

MIMO-aided Irregular Repetition Schemes for Mission Critical Communications

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MIMO-aided Irregular Repetition Schemes for Mission Critical...

IEEE Conference on Vehicular Technology (VTC)

作者： Linlin Zhao Shaodan Ma Guanghua Yang Xuefen Chi Wanting Yang Department of Communications Engineering Jilin University Changchun China Yibin Institute of Jilin University Yibin China Department of Electrical and Computer Engineering University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China

Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.

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