196,199 results on '"LI, Li"'
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52. 5. Shaping the “Red Classics' of Chinese Art in Early Socialist China: Manipulating Tradition to Establish New Guohua
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Roberts, Rosemary and Li, Li
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- 2018
53. 4. How Is Revolution “Popularized'?: Rereading Tracks in the Snowy Forest
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Roberts, Rosemary and Li, Li
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- 2018
54. 2. Great Changes in Critical Reception: “Red Classic' Authenticity and the “Eight Black Theories'
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Roberts, Rosemary and Li, Li
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- 2018
55. 3. How to Tell a Story of Imprisonment: Ideology, Truth, and Melodramatic Body in the Making of Red Crag
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Roberts, Rosemary and Li, Li
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- 2018
56. 1. The “Red Classic' That Never Was: Wang Lin’s Hinterland
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Roberts, Rosemary and Li, Li
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- 2018
57. PART I - Creating the Canon: The “Red Classics' in the Maoist Era
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Roberts, Rosemary and Li, Li
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- 2018
58. Acknowledgments
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Roberts, Rosemary and Li, Li
- Published
- 2018
59. IVCA: Inter-Relation-Aware Video Complexity Analyzer
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Liao, Junqi, Li, Yao, Li, Zhuoyuan, Li, Li, and Liu, Dong
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
To meet the real-time analysis requirements of video streaming applications, we propose an inter-relation-aware video complexity analyzer (IVCA) as an extension to VCA. The IVCA addresses the limitation of VCA by considering inter-frame relations, namely motion and reference structure. First, we enhance the accuracy of temporal features by introducing feature-domain motion estimation into the IVCA. Next, drawing inspiration from the hierarchical reference structure in codecs, we design layer-aware weights to adjust the majorities of frame complexity in different layers. Additionally, we expand the scope of temporal features by considering frames that be referred to, rather than relying solely on the previous frame. Experimental results show the significant improvement in complexity estimation accuracy achieved by IVCA, with minimal time complexity increase., Comment: The report for the solution of second prize winner in ICIP 2024 Grand Challenge on Video Complexity (Team: USTC-iVC_Team1, USTC-iVC_Team2)
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- 2024
60. Prediction and Reference Quality Adaptation for Learned Video Compression
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Sheng, Xihua, Li, Li, Liu, Dong, and Li, Houqiang
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Temporal prediction is one of the most important technologies for video compression. Various prediction coding modes are designed in traditional video codecs. Traditional video codecs will adaptively to decide the optimal coding mode according to the prediction quality and reference quality. Recently, learned video codecs have made great progress. However, they ignore the prediction and reference quality adaptation, which leads to incorrect utilization of temporal prediction and reconstruction error propagation. Therefore, in this paper, we first propose a confidence-based prediction quality adaptation (PQA) module to provide explicit discrimination for the spatial and channel-wise prediction quality difference. With this module, the prediction with low quality will be suppressed and that with high quality will be enhanced. The codec can adaptively decide which spatial or channel location of predictions to use. Then, we further propose a reference quality adaptation (RQA) module and an associated repeat-long training strategy to provide dynamic spatially variant filters for diverse reference qualities. With the filters, it is easier for our codec to achieve the target reconstruction quality according to reference qualities, thus reducing the propagation of reconstruction errors. Experimental results show that our codec obtains higher compression performance than the reference software of H.266/VVC and the previous state-of-the-art learned video codecs in both RGB and YUV420 colorspaces.
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- 2024
61. Improved Remixing Process for Domain Adaptation-Based Speech Enhancement by Mitigating Data Imbalance in Signal-to-Noise Ratio
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Li, Li and Seki, Shogo
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Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
RemixIT and Remixed2Remixed are domain adaptation-based speech enhancement (DASE) methods that use a teacher model trained in full supervision to generate pseudo-paired data by remixing the outputs of the teacher model. The student model for enhancing real-world recorded signals is trained using the pseudo-paired data without ground truth. Since the noisy signals are recorded in natural environments, the dataset inevitably suffers data imbalance in some acoustic properties, leading to subpar performance for the underrepresented data. The signal-to-noise ratio (SNR), inherently balanced in supervised learning, is a prime example. In this paper, we provide empirical evidence that the SNR of pseudo data has a significant impact on model performance using the dataset of the CHiME-7 UDASE task, highlighting the importance of balanced SNR in DASE. Furthermore, we propose adopting curriculum learning to encompass a broad range of SNRs to boost performance for underrepresented data., Comment: Accepted at Interspeech2024
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- 2024
62. Einstein-Podolsky-Rosen Steering Criterion and Monogamy Relation via Correlation Matrices in Tripartite Systems
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Li, Li-Juan, Fan, Xiao-Gang, Song, Xue-Ke, Ye, Liu, and Wang, Dong
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Quantum Physics - Abstract
Quantum steering is considered as one of the most well-known nonlocal phenomena in quantum mechanics. Unlike entanglement and Bell non-locality, the asymmetry of quantum steering makes it vital for one-sided device-independent quantum information processing. Although there has been much progress on steering detection for bipartite systems, the criterion for EPR steering in tripartite systems remains challenging and inadequate. In this paper, we firstly derive a novel and promising steering criterion for any three-qubit states via correlation matrix. Furthermore, we propose the monogamy relation between the tripartite steering of system and the bipartite steering of subsystems based on the derived criterion. Finally, as illustrations, we demonstrate the performance of the steering criterion and the monogamy relation by means of several representative examples. We believe that the results and methods presented in this work could be beneficial to capture genuine multipartite steering in the near future., Comment: 10 pages, 4 figures, comments are welcomed. Accepted by Physical Review A
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- 2024
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63. Quantum analog to flapping of flags: interface instability for co-flow binary superfluids
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An, Yuping, Li, Li, and Zeng, Huabi
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Condensed Matter - Quantum Gases ,General Relativity and Quantum Cosmology ,High Energy Physics - Theory - Abstract
We study the interface dynamics in immiscible binary superfluids using its holographic description, which naturally consists of an inviscid superfluid component and a viscous normal fluid component. We give the first theoretical realization of interface instability for two superfluid components moving with identical velocity, providing a quantum analog to the flapping of flags that is common in daily life. This behavior is in sharp contrast to the one from Gross-Pitaevskii equation for which no such co-flow instability develops in an isolated uniform system because of Galilean invariance. The real time evolution triggered by the dynamical instability exhibits intricate nonlinear patterns leading to quantum turbulence reminiscent of the quantum Kelvin-Helmholtz instability. Moreover, we show that such interface dynamics is essentially different from the Landau instability for which the frictionless flow becomes thermodynamically unstable above a critical superfluid velocity. Our study uncovers the rich interface dynamics of quantum fluids and the emergence of complex flow phenomena., Comment: 19 pages, 7 figures
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- 2024
64. QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning
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Cai, Rong-Gen, He, Song, Li, Li, and Zeng, Hong-An
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High Energy Physics - Theory ,General Relativity and Quantum Cosmology ,High Energy Physics - Phenomenology ,Nuclear Theory - Abstract
By leveraging neural networks, we address the inverse problem of constructing a quantitative 2+1-flavor holographic QCD model based on state-of-the-art lattice QCD data. Our model demonstrates quantitative agreement with the latest lattice QCD results. We construct the full phase diagram at finite magnetic field $B$, baryon chemical potential $\mu_B$ and temperature $T$. We uncover rich phase structure with a first-order phase transition surface and a critical endpoint line within the 3-dimensional phase diagram. The critical endpoint at vanishing chemical potential aligns with current speculations in the lattice QCD literature. In particular, for large magnetic field, we find two critical endpoints in the $T$-$\mu_B$ plane. The critical exponents of the critical endpoints adhere to scaling relations and depend on the background magnetic field. Moreover, they are exhibit deviations from mean-field theory, highlighting the distinctive features of our holographic approach., Comment: 10 pages, 13 figures
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- 2024
65. DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
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Li, Li, Ismail, Khalid N., Shum, Hubert P. H., and Breckon, Toby P.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix stereo camera, a lux meter and a GNSS/INS system. Ambient and reflectivity images are made available along with the LiDAR point clouds to facilitate multi-modal use of concurrent ambient and reflectivity scene information. Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased availability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self-supervised loss formulation. We compare performance over both our new DurLAR dataset, the established KITTI benchmark and the Cityscapes dataset. Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution and availability within DurLAR improves the quantitative and qualitative performance of leading contemporary monocular depth estimation approaches (RMSE=3.639, Sq Rel=0.936)., Comment: Accepted by 3DV 2021; 13 pages, 14 figures; Dataset at https://github.com/l1997i/durlar
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- 2024
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66. Direct observations of cross-scale energy transfer in space plasmas
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Li, Jing-Huan, Zhou, Xu-Zhi, Liu, Zhi-Yang, Wang, Shan, Omura, Yoshiharu, Li, Li, Yue, Chao, Zong, Qiu-Gang, Le, Guan, Russell, Christopher T., and Burch, James L.
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Physics - Space Physics - Abstract
The collisionless plasmas in space and astrophysical environments are intrinsically multiscale in nature, behaving as conducting fluids at macroscales and kinetically at microscales comparable to ion- and/or electron-gyroradii. A fundamental question in understanding the plasma dynamics is how energy is transported and dissipated across different scales. Here, we present spacecraft measurements in the solar wind upstream of the terrestrial bow shock, in which the macroscale ultra-low-frequency waves and microscale whistler waves simultaneously resonate with the ions. The ion acceleration from ultra-low-frequency waves leads to velocity distributions unstable to the growth of whistler waves, which in turn resonate with the electrons to complete cross-scale energy transfer. These observations, consistent with numerical simulations in the occurrence of phase-bunched ion and electron distributions, also highlight the importance of anomalous resonance, a nonlinear modification of the classical cyclotron resonance, in the cross-scale wave coupling and energy transfer processes., Comment: 22 pages, 7 figures and supplementary material
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- 2024
67. Async Learned User Embeddings for Ads Delivery Optimization
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Tang, Mingwei, Liu, Meng, Li, Hong, Yang, Junjie, Wei, Chenglin, Li, Boyang, Li, Dai, Xu, Rengan, Xu, Yifan, Zhang, Zehua, Wang, Xiangyu, Liu, Linfeng, Xie, Yuelei, Liu, Chengye, Fawaz, Labib, Li, Li, Wang, Hongnan, Zhu, Bill, and Reddy, Sri
- Subjects
Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance. The effectiveness of recommendation systems depends on the quality of user embedding. We propose to asynchronously learn high fidelity user embeddings for billions of users each day from sequence based multimodal user activities through a Transformer-like large scale feature learning module. The async learned user representations embeddings (ALURE) are further converted to user similarity graphs through graph learning and then combined with user realtime activities to retrieval highly related ads candidates for the ads delivery system. Our method shows significant gains in both offline and online experiments., Comment: Accepted by workshop on Multimodal Representation and Retrieval at SIGIR 2024, Washington DC
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- 2024
68. Error Bounds of Supervised Classification from Information-Theoretic Perspective
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Qi, Binchuan, Gong, Wei, and Li, Li
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Computer Science - Machine Learning ,Computer Science - Information Retrieval - Abstract
There remains a list of unanswered research questions on deep learning (DL), including the remarkable generalization power of overparametrized neural networks, the efficient optimization performance despite the non-convexity, and the mechanisms behind flat minima in generalization. In this paper, we adopt an information-theoretic perspective to explore the theoretical foundations of supervised classification using deep neural networks (DNNs). Our analysis introduces the concepts of fitting error and model risk, which, together with generalization error, constitute an upper bound on the expected risk. We demonstrate that the generalization errors are bounded by the complexity, influenced by both the smoothness of distribution and the sample size. Consequently, task complexity serves as a reliable indicator of the dataset's quality, guiding the setting of regularization hyperparameters. Furthermore, the derived upper bound fitting error links the back-propagated gradient, Neural Tangent Kernel (NTK), and the model's parameter count with the fitting error. Utilizing the triangle inequality, we establish an upper bound on the expected risk. This bound offers valuable insights into the effects of overparameterization, non-convex optimization, and the flat minima in DNNs.Finally, empirical verification confirms a significant positive correlation between the derived theoretical bounds and the practical expected risk, confirming the practical relevance of the theoretical findings.
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- 2024
69. Entanglement engineering of optomechanical systems by reinforcement learning
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Ye, Li-Li, Arenz, Christian, Lukens, Joseph M., and Lai, Ying-Cheng
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Quantum Physics ,Physics - Applied Physics ,Physics - Optics - Abstract
Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement -- so-called entanglement engineering -- for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon-phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general., Comment: 17 pages, 10 figures
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- 2024
70. Towards Federated Domain Unlearning: Verification Methodologies and Challenges
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Tam, Kahou, Xu, Kewei, Li, Li, and Fu, Huazhu
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Federated Learning (FL) has evolved as a powerful tool for collaborative model training across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and finance. However, the introduction of the Right to Be Forgotten (RTBF) poses new challenges, necessitating federated unlearning to delete data without full model retraining. Traditional FL unlearning methods, not originally designed with domain specificity in mind, inadequately address the complexities of multi-domain scenarios, often affecting the accuracy of models in non-targeted domains or leading to uniform forgetting across all domains. Our work presents the first comprehensive empirical study on Federated Domain Unlearning, analyzing the characteristics and challenges of current techniques in multi-domain contexts. We uncover that these methods falter, particularly because they neglect the nuanced influences of domain-specific data, which can lead to significant performance degradation and inaccurate model behavior. Our findings reveal that unlearning disproportionately affects the model's deeper layers, erasing critical representational subspaces acquired during earlier training phases. In response, we propose novel evaluation methodologies tailored for Federated Domain Unlearning, aiming to accurately assess and verify domain-specific data erasure without compromising the model's overall integrity and performance. This investigation not only highlights the urgent need for domain-centric unlearning strategies in FL but also sets a new precedent for evaluating and implementing these techniques effectively., Comment: 16 pages, 12 figures
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- 2024
71. Activity-driven polymer knotting for macromolecular topology engineering
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Li, Jia-Xiang, Wu, Song, Hao, Li-Li, Lei, Qun-Li, and Ma, Yu-Qiang
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Condensed Matter - Soft Condensed Matter - Abstract
Macromolecules can gain special properties by adopting knotted conformations, but engineering knotted macromolecules is a challenging task. Here we surprisingly observed that knotting can be very effectively produced in active polymers. When one end of an actively reptative polymer is anchored, it can undergo continual self-knotting as a result of intermittent giant conformation fluctuations and the outward reptative motion. Once a knot is formed, it migrates to the anchored point due to a non-equilibrium ratchet effect. Moreover, when the active polymer is grafted on the end of a passive polymer, it can function as a self-propelling soft needle to either transfer its own knots to the passive polymer or directly braid knots on the passive polymer. We further show that these active needles can create inter-molecular bridging knots between two passive polymers. Our finding highlights the non-equilibrium effects in modifying the dynamic pathways of polymer systems, which have potential applications in macromolecular topology engineering, e.g., manipulating topological states of proteins and nucleic acids, as well as macromolecular braiding.
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- 2024
72. Identification of coupled Landau and anomalous resonances in space plasmas
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Li, Jing-Huan, Zhou, Xu-Zhi, Liu, Zhi-Yang, Wang, Shan, Artemyev, Anton V., Omura, Yoshiharu, Zhang, Xiao-Jia, Li, Li, Yue, Chao, Zong, Qiu-Gang, Pollock, Craig, Le, Guan, and Burch, James L.
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Physics - Space Physics ,Physics - Plasma Physics - Abstract
Wave-particle resonance, a ubiquitous process in the plasma universe, occurs when resonant particles observe a constant wave phase to enable sustained energy transfer. Here, we present spacecraft observations of simultaneous Landau and anomalous resonances between oblique whistler waves and the same group of protons, which are evidenced, respectively, by phase-space rings in parallel-velocity spectra and phase-bunched distributions in gyro-phase spectra. Our results indicate the coupling between Landau and anomalous resonances via the overlapping of the resonance islands., Comment: 13 pages, 4 figures and supplementary material
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- 2024
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73. An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
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Sun, Jiahao, Qing, Chunmei, Xu, Xiang, Kong, Lingdong, Liu, Youquan, Li, Li, Zhu, Chenming, Zhang, Jingwei, Xiao, Zeqi, Chen, Runnan, Wang, Tai, Zhang, Wenwei, and Chen, Kai
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on disparate, standalone codebases, hindering unified advancements and fair benchmarking across models. To address these challenges, we introduce MMDetection3D-lidarseg, a comprehensive toolbox designed for the efficient training and evaluation of state-of-the-art LiDAR segmentation models. We support a wide range of segmentation models and integrate advanced data augmentation techniques to enhance robustness and generalization. Additionally, the toolbox provides support for multiple leading sparse convolution backends, optimizing computational efficiency and performance. By fostering a unified framework, MMDetection3D-lidarseg streamlines development and benchmarking, setting new standards for research and application. Our extensive benchmark experiments on widely-used datasets demonstrate the effectiveness of the toolbox. The codebase and trained models have been publicly available, promoting further research and innovation in the field of LiDAR segmentation for autonomous driving., Comment: Preprint; 17 pages, 4 figures, 7 tables; Code at https://github.com/open-mmlab/mmdetection3d
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- 2024
74. Backpropagation-Free Multi-modal On-Device Model Adaptation via Cloud-Device Collaboration
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Ji, Wei, Li, Li, Lv, Zheqi, Zhang, Wenqiao, Li, Mengze, Wan, Zhen, Lei, Wenqiang, and Zimmermann, Roger
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Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In our increasingly interconnected world, where intelligent devices continually amass copious personalized multi-modal data, a pressing need arises to deliver high-quality, personalized device-aware services. However, this endeavor presents a multifaceted challenge to prevailing artificial intelligence (AI) systems primarily rooted in the cloud. As these systems grapple with shifting data distributions between the cloud and devices, the traditional approach of fine-tuning-based adaptation (FTA) exists the following issues: the costly and time-consuming data annotation required by FTA and the looming risk of model overfitting. To surmount these challenges, we introduce a Universal On-Device Multi-modal Model Adaptation Framework, revolutionizing on-device model adaptation by striking a balance between efficiency and effectiveness. The framework features the Fast Domain Adaptor (FDA) hosted in the cloud, providing tailored parameters for the Lightweight Multi-modal Model on devices. To enhance adaptability across multi-modal tasks, the AnchorFrame Distribution Reasoner (ADR) minimizes communication costs. Our contributions, encapsulated in the Cloud-Device Collaboration Multi-modal Parameter Generation (CDC-MMPG) framework, represent a pioneering solution for on-Device Multi-modal Model Adaptation (DMMA). Extensive experiments validate the efficiency and effectiveness of our method, particularly in video question answering and retrieval tasks, driving forward the integration of intelligent devices into our daily lives.
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- 2024
75. SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge
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Wang, Andong, Wu, Bo, Chen, Sunli, Chen, Zhenfang, Guan, Haotian, Lee, Wei-Ning, Li, Li Erran, and Gan, Chuang
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations, specifically within dynamic, open-world, and structured context knowledge. We propose a new benchmark (SOK-Bench), consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset, we propose an automatic and scalable generation method to generate question-answer pairs, knowledge graphs, and rationales by instructing the combinations of LLMs and MLLMs. Concretely, we first extract observable situated entities, relations, and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense, we generate associated question-answer pairs and reasoning processes, finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision-language models on the benchmark and found several insightful conclusions. For more information, please refer to our benchmark at www.bobbywu.com/SOKBench., Comment: CVPR
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- 2024
76. Nakajima's quiver varieties and triangular bases of bipartite cluster algebras
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Li, Li
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Mathematics - Representation Theory ,Mathematics - Algebraic Geometry ,Primary 13F60, Secondary 14F06, 16G20, 32S60 - Abstract
Berenstein and Zelevinsky introduced quantum cluster algebras \cite{BZ1} and the triangular bases \cite{BZ2}. The support conjecture proposed in \cite{LLRZ}, which asserts that the support of each triangular basis element for a rank-2 cluster algebra is bounded by an explicitly described region, was established in \cite{L} for skew-symmetric rank-2 cluster algebras. In this paper we extend this result by proving a bound on the support of each triangular basis element for bipartite cluster algebras., Comment: arXiv admin note: text overlap with arXiv:2208.12307
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- 2024
77. FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
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Ning, Zhiyuan, Tian, Chunlin, Xiao, Meng, Fan, Wei, Wang, Pengyang, Li, Li, Wang, Pengfei, and Zhou, Yuanchun
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short of addressing these complexities holistically. In response, we propose FedGCS, a novel generative client selection framework that innovatively recasts the client selection process as a generative task. Drawing inspiration from the methodologies used in large language models, FedGCS efficiently encodes abundant decision-making knowledge within a continuous representation space, enabling efficient gradient-based optimization to search for optimal client selection that will be finally output via generation. The framework comprises four steps: (1) automatic collection of diverse "selection-score" pair data using classical client selection methods; (2) training an encoder-evaluator-decoder framework on this data to construct a continuous representation space; (3) employing gradient-based optimization in this space for optimal client selection; (4) generating the final optimal client selection via using beam search for the well-trained decoder. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. The effectiveness of FedGCS is proven through extensive experimental analyses., Comment: Accepted by IJCAI-2024
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- 2024
78. MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization
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Zhu, Pengcheng, Zhuang, Yaoming, Chen, Baoquan, Li, Li, Wu, Chengdong, and Liu, Zhanlin
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
This letter introduces a novel framework for dense Visual Simultaneous Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently Gaussian Splatting-based SLAM has yielded promising results, but rely on RGB-D input and is weak in tracking. To address these limitations, we uniquely integrates advanced sparse visual odometry with a dense Gaussian Splatting scene representation for the first time, thereby eliminating the dependency on depth maps typical of Gaussian Splatting-based SLAM systems and enhancing tracking robustness. Here, the sparse visual odometry tracks camera poses in RGB stream, while Gaussian Splatting handles map reconstruction. These components are interconnected through a Multi-View Stereo (MVS) depth estimation network. And we propose a depth smooth loss to reduce the negative effect of estimated depth maps. Furthermore, the consistency in scale between the sparse visual odometry and the dense Gaussian map is preserved by Sparse-Dense Adjustment Ring (SDAR). We have evaluated our system across various synthetic and real-world datasets. The accuracy of our pose estimation surpasses existing methods and achieves state-of-the-art performance. Additionally, it outperforms previous monocular methods in terms of novel view synthesis fidelity, matching the results of neural SLAM systems that utilize RGB-D input., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
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- 2024
79. Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
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Tian, Chunlin, Shi, Zhan, Qin, Xinpeng, Li, Li, and Xu, Chengzhong
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To address these challenges, we introduce a novel device selection solution called FedRank, which is an end-to-end, ranking-based approach that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that \model~ boosts model accuracy by 5.2\% to 56.9\%, accelerates the training convergence up to $2.01 \times$ and saves the energy consumption up to $40.1\%$., Comment: Accepted by ICML 2024
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- 2024
80. DMOFC: Discrimination Metric-Optimized Feature Compression
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Gao, Changsheng, Jiang, Yiheng, Li, Li, Liu, Dong, and Wu, Feng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Feature compression, as an important branch of video coding for machines (VCM), has attracted significant attention and exploration. However, the existing methods mainly focus on intra-feature similarity, such as the Mean Squared Error (MSE) between the reconstructed and original features, while neglecting the importance of inter-feature relationships. In this paper, we analyze the inter-feature relationships, focusing on feature discriminability in machine vision and underscoring its significance in feature compression. To maintain the feature discriminability of reconstructed features, we introduce a discrimination metric for feature compression. The discrimination metric is designed to ensure that the distance between features of the same category is smaller than the distance between features of different categories. Furthermore, we explore the relationship between the discrimination metric and the discriminability of the original features. Experimental results confirm the effectiveness of the proposed discrimination metric and reveal there exists a trade-off between the discrimination metric and the discriminability of the original features.
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- 2024
81. Leverage Multi-source Traffic Demand Data Fusion with Transformer Model for Urban Parking Prediction
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Huang, Yin, Dong, Yongqi, Tang, Youhua, and Li, Li
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Emerging Technologies - Abstract
The escalation in urban private car ownership has worsened the urban parking predicament, necessitating effective parking availability prediction for urban planning and management. However, the existing prediction methods suffer from low prediction accuracy with the lack of spatial-temporal correlation features related to parking volume, and neglect of flow patterns and correlations between similar parking lots within certain areas. To address these challenges, this study proposes a parking availability prediction framework integrating spatial-temporal deep learning with multi-source data fusion, encompassing traffic demand data from multiple sources (e.g., metro, bus, taxi services), and parking lot data. The framework is based on the Transformer as the spatial-temporal deep learning model and leverages K-means clustering to establish parking cluster zones, extracting and integrating traffic demand characteristics from various transportation modes (i.e., metro, bus, online ride-hailing, and taxi) connected to parking lots. Real-world empirical data was used to verify the effectiveness of the proposed method compared with different machine learning, deep learning, and traditional statistical models for predicting parking availability. Experimental results reveal that, with the proposed pipeline, the developed Transformer model outperforms other models in terms of various metrics, e.g., Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). By fusing multi-source demanding data with spatial-temporal deep learning techniques, this approach offers the potential to develop parking availability prediction systems that furnish more accurate and timely information to both drivers and urban planners, thereby fostering more efficient and sustainable urban mobility., Comment: 7 pages, 5 figures, under review by the 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024)
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- 2024
82. Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography
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Han, Chen-Di, Ye, Li-Li, Lin, Zin, Kovanis, Vassilios, and Lai, Ying-Cheng
- Subjects
Physics - Computational Physics ,Condensed Matter - Materials Science ,Physics - Applied Physics ,Physics - Optics ,Quantum Physics - Abstract
Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95$\%$, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation, designing broadband, and multi-functionality graphene metasurface systems are illustrated., Comment: 13 pages, 9 figures
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- 2024
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83. HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
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Tian, Chunlin, Shi, Zhan, Guo, Zhijiang, Li, Li, and Xu, Chengzhong
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Adapting Large Language Models (LLMs) to new tasks through fine-tuning has been made more efficient by the introduction of Parameter-Efficient Fine-Tuning (PEFT) techniques, such as LoRA. However, these methods often underperform compared to full fine-tuning, particularly in scenarios involving complex datasets. This issue becomes even more pronounced in complex domains, highlighting the need for improved PEFT approaches that can achieve better performance. Through a series of experiments, we have uncovered two critical insights that shed light on the training and parameter inefficiency of LoRA. Building on these insights, we have developed HydraLoRA, a LoRA framework with an asymmetric structure that eliminates the need for domain expertise. Our experiments demonstrate that HydraLoRA outperforms other PEFT approaches, even those that rely on domain knowledge during the training and inference phases., Comment: 19 pages, 7 figures
- Published
- 2024
84. Tunable coupling of a quantum phononic resonator to a transmon qubit with flip-chip architecture
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Ruan, Xinhui, Li, Li, Liang, Guihan, Zhao, Silu, Wang, Jia-heng, Bu, Yizhou, Chen, Bingjie, Song, Xiaohui, Li, Xiang, Zhang, He, Wang, Jinzhe, Zhao, Qianchuan, Xu, Kai, Fan, Heng, Liu, Yu-xi, Zhang, Jing, Peng, Zhihui, Xiang, Zhongcheng, and Zheng, Dongning
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Quantum Physics - Abstract
A hybrid system with tunable coupling between phonons and qubits shows great potential for advancing quantum information processing. In this work, we demonstrate strong and tunable coupling between a surface acoustic wave (SAW) resonator and a transmon qubit based on galvanic-contact flip-chip technique. The coupling strength varies from $2\pi\times$7.0 MHz to -$2\pi\times$20.6 MHz, which is extracted from different vacuum Rabi oscillation frequencies. The phonon-induced ac Stark shift of the qubit at different coupling strengths is also shown. Our approach offers a good experimental platform for exploring quantum acoustics and hybrid systems.
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- 2024
85. AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
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Sun, Zhensu, Du, Xiaoning, Yang, Zhou, Li, Li, and Lo, David
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Computer Science - Software Engineering ,Computer Science - Artificial Intelligence ,Computer Science - Programming Languages - Abstract
Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and layout of current programs are designed to cater the needs of human developers -- with many grammar tokens and formatting tokens being used to make the code easier for humans to read. While this is helpful, such a design adds unnecessary computational work for LLMs, as each token they either use or produce consumes computational resources. To improve inference efficiency and reduce computational costs, we propose the concept of AI-oriented grammar. This aims to represent code in a way that better suits the working mechanism of AI models. Code written with AI-oriented grammar discards formats and uses a minimum number of tokens to convey code semantics effectively. To demonstrate the feasibility of this concept, we explore and implement the first AI-oriented grammar for Python, named SimPy. SimPy is crafted by revising the original Python grammar through a series of heuristic rules. Programs written in SimPy maintain identical AST structures to those in standard Python. This allows for not only execution via a modified AST parser, but also seamless transformation between programs written in Python and SimPy, enabling human developers and LLMs to use Python and SimPy, respectively, when they need to collaborate. In the experiments, compared with Python, SimPy enables a reduction in token usage by 13.5% and 10.4% for CodeLlama and GPT-4, respectively, when completing the same set of code-related tasks. Additionally, these models can maintain or even improve their performance when using SimPy instead of Python for these tasks., Comment: Accepted by ISSTA'24
- Published
- 2024
86. Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
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Wu, Yebo, Li, Li, Tian, Chunlin, and Xu, Chengzhong
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Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Machine Learning - Abstract
This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training round, ProFL first trains the front blocks and safely freezes them after convergence. Training of the next block is then triggered. This process iterates until the training of the whole model is completed. In this way, the memory footprint is effectively reduced for feasible deployment on heterogeneous devices. In order to preserve the feature representation of each block, we decouple the whole training process into two stages: progressive model shrinking and progressive model growing. During the progressive model shrinking stage, we meticulously design corresponding output modules to assist each block in learning the expected feature representation and obtain the initialization parameters. Then, the obtained output modules are utilized in the corresponding progressive model growing stage. Additionally, to control the training pace for each block, a novel metric from the scalar perspective is proposed to assess the learning status of each block and determines when to trigger the training of the next one. Finally, we theoretically prove the convergence of ProFL and conduct extensive experiments on representative models and datasets to evaluate the effectiveness of ProFL. The results demonstrate that ProFL effectively reduces the peak memory footprint by up to 57.4% and improves model accuracy by up to 82.4%.
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- 2024
87. Revisiting holographic model for thermal and dense QCD with a critical point
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Fu, Qingxuan, He, Song, Li, Li, and Li, Zhibin
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High Energy Physics - Phenomenology ,High Energy Physics - Theory - Abstract
To quantitatively provide reliable predictions for the hot and dense QCD matter, a holographic model should be adjusted to describe first-principles lattice results available at vanishing baryon chemical potential. The equation of state from two well-known lattice groups, the HotQCD collaboration and the Wuppertal-Budapest (WB) collaboration, shows visible differences at high temperatures. We revisit the Einstein-Maxwell-dilaton (EMD) holographic model for hot QCD with 2+1 flavors and physical quark masses by fitting lattice QCD data from the WB collaboration. Using the parameterization for the scalar potential and gauge coupling proposed in our work [Phys.Rev.D 106 (2022) 12, L121902], the equation of state, the higher order baryon number susceptibilities, and the chiral condensates are in quantitative agreement with state-of-the-art lattice results. We find that the critical endpoint (CEP) obtained from fitting the WB collaboration data is nearly identical to the one from the HotQCD collaboration, suggesting the robustness of the location of the CEP. Moreover, our holographic prediction for the CEP location is in accord with more recent Bayesian analysis on a large number of holographic EMD models and an effective potential approach of QCD from gap equations.
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- 2024
88. Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
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Lin, Zhihao, Ma, Wei, Lin, Tao, Zheng, Yaowen, Ge, Jingquan, Wang, Jun, Klein, Jacques, Bissyande, Tegawende, Liu, Yang, and Li, Li
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Computer Science - Software Engineering ,Computer Science - Artificial Intelligence - Abstract
Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like traditional SE tools, open-source collaboration is key in realising the excellent products. However, with AI models, the essential need is in data. The collaboration of these AI-based SE models hinges on maximising the sources of high-quality data. However, data especially of high quality, often holds commercial or sensitive value, making it less accessible for open-source AI-based SE projects. This reality presents a significant barrier to the development and enhancement of AI-based SE tools within the software engineering community. Therefore, researchers need to find solutions for enabling open-source AI-based SE models to tap into resources by different organisations. Addressing this challenge, our position paper investigates one solution to facilitate access to diverse organizational resources for open-source AI models, ensuring privacy and commercial sensitivities are respected. We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security. Additionally, we present guidelines for developers on AI-based SE tool collaboration, covering data requirements, model architecture, updating strategies, and version control. Given the significant influence of data characteristics on FL, our research examines the effect of code data heterogeneity on FL performance.
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- 2024
89. Learning Multidimensional Disentangled Representations of Instrumental Sounds for Musical Similarity Assessment
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Hashizume, Yuka, Li, Li, Miyashita, Atsushi, and Toda, Tomoki
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Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
To achieve a flexible recommendation and retrieval system, it is desirable to calculate music similarity by focusing on multiple partial elements of musical pieces and allowing the users to select the element they want to focus on. A previous study proposed using multiple individual networks for calculating music similarity based on each instrumental sound, but it is impractical to use each signal as a query in search systems. Using separated instrumental sounds alternatively resulted in less accuracy due to artifacts. In this paper, we propose a method to compute similarities focusing on each instrumental sound with a single network that takes mixed sounds as input instead of individual instrumental sounds. Specifically, we design a single similarity embedding space with disentangled dimensions for each instrument, extracted by Conditional Similarity Networks, which is trained by the triplet loss using masks. Experimental results have shown that (1) the proposed method can obtain more accurate feature representation than using individual networks using separated sounds as input, (2) each sub-embedding space can hold the characteristics of the corresponding instrument, and (3) the selection of similar musical pieces focusing on each instrumental sound by the proposed method can obtain human consent, especially in drums and guitar.
- Published
- 2024
90. Enforcement of Amended Criminal Procedure Law in China: Unbalanced Power Relations between Public and Private Participants
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Li, Li and Guo, Tianwu
- Published
- 2019
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91. A flipped class to support the success of at-risk students
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Voon, Li Li, Leong, Siow Hoo, and Liew, Chin Ying
- Published
- 2024
92. Development of an Emergency Department–Based Intervention to Expand Access to Medications for Opioid Use Disorder in a Medicaid Nonexpansion Setting: Protocol for Engagement and Community Collaboration
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Walter, Lauren A, Li, Li, Rodgers, Joel B, Hess, Jennifer J, Skains, Rachel M, Delaney, Matthew C, Booth, James, and Hess, Erik P
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Medicine ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
BackgroundThe opioid epidemic has disproportionately impacted areas in the Appalachian region of the United States. Characterized by persistent Medicaid nonexpansion, higher poverty rates, and health care access challenges, populations residing in these areas of the United States have experienced higher opioid overdose death rates than those in other parts of the country. Jefferson County, Alabama, located in Southern Appalachia, has been especially affected, with overdose rates over 2 times greater than the statewide average (48.8 vs 19.9 overdoses per 10,000 persons). Emergency departments (EDs) have been recognized as a major health care source for persons with opioid use disorder (OUD). A program to initiate medications for OUD in the ED has been shown to be effective in treatment retention. Likewise, continued patient engagement in a recovery or treatment program after ED discharge has been shown to be efficient for long-term treatment success. ObjectiveThis protocol outlines a framework for ED-initiated medications for OUD in a resource-limited region of the United States; the study will be made possible through community partnerships with referral resources for definitive OUD care. MethodsWhen a patient presents to the ED with symptoms of opioid withdrawal, nonfatal opioid overdose, or requesting opioid detoxification, clinicians will consider the diagnosis of OUD using the Diagnostic and Statistical Manual of Mental Disorders (fifth edition) criteria. All patients meeting the diagnostic criteria for moderate to severe OUD will be further engaged and assessed for study eligibility. Recruited subjects will be evaluated for signs and symptoms of withdrawal, treated with buprenorphine-naloxone as appropriate, and given a prescription for take-home induction along with an intranasal naloxone kit. At the time of ED discharge, a peer navigator from a local substance use coordinating center will be engaged to facilitate patient referral to a regional substance abuse coordinating center for longitudinal addiction treatment. ResultsThis project is currently ongoing; it received funding in February 2019 and was approved by the institutional review board of the University of Alabama at Birmingham in June 2019. Data collection began on July 7, 2019, with a projected end date in February 2022. In total, 79 subjects have been enrolled to date. Results will be published in the summer of 2022. ConclusionsED recognition of OUD accompanied by buprenorphine-naloxone induction and referral for subsequent long-term treatment engagement have been shown to be components of an effective strategy for addressing the ongoing opioid crisis. Establishing community and local partnerships, particularly in resource-limited areas, is crucial for the continuity of addiction care and rehabilitation outcomes. International Registered Report Identifier (IRRID)DERR1-10.2196/18734
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- 2021
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93. Towards a Machine Learning-Based Constructive Alignment Approach for Improving Outcomes Composure of Engineering Curriculum
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Wai Tong Chor, Kam Meng Goh, Li Li Lim, Kin Yun Lum, and Tsung Heng Chiew
- Abstract
The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency-Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching-learning activities are aligned with regulatory body expectations.
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- 2024
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94. New Opportunities for Electric Fields in Promoting Wound Healing: Collective Electrotaxis
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Zhang, Yan, Huang, Shiwen, Cao, Yifei, Li, Li, Yang, Jun, and Zhao, Min
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Engineering ,Biomedical Engineering ,Bioengineering ,Biochemistry and Cell Biology ,Medical Biotechnology ,Medical biotechnology ,Biomedical engineering - Abstract
SignificanceIt has long been hypothesized that naturally occurring electric fields (EFs) aid wound healing by guiding cell migration. Consequently, the application of EFs has significant potential for promoting wound healing. However, the mechanisms underlying the cellular response to EFs remain unclear. Recent Advances: Although the directed migration of isolated single cells under EFs has been studied for decades, only recently has experimental evidence demonstrated the distinct collective migration of large sheets of keratinocytes and corneal epithelial cells in response to applied EFs. Accumulating evidence suggests that the emergent properties of cell groups in response to EF guidance offer new opportunities for EF-assisted directional migration.Critical issuesIn this review, we provide an overview of the field of collective electrotaxis, highlighting key advances made in recent years. We also discuss advanced engineering strategies utilized to manipulate collective electrotaxis.Future directionsWe outline a series of unanswered questions in this field and propose potential applications of collective electrotaxis in developing electrical stimulation technologies for wound healing.
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- 2024
95. Shifting groundwater fluxes in bedrock fractures: Evidence from stream water radon and water isotopes
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Johnson, Keira, Christensen, John N, Gardner, W Payton, Sprenger, Matthias, Li, Li, Williams, Kenneth H, Carroll, Rosemary WH, Thiros, Nicholas, Brown, Wendy, Beutler, Curtis, Newman, Alexander, and Sullivan, Pamela L
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Hydrology ,Earth Sciences ,Geology ,Groundwater surface water interactions ,Tracer hydrology ,Groundwater modeling ,Groundwater discharge ,Montane catchment ,Environmental Engineering - Abstract
Geologic features (e.g., fractures and alluvial fans) can play an important role in the locations and volumes of groundwater discharge and degree of groundwater-surface water (GW-SW) interactions. However, the role of these features in controlling GW-SW dynamics and streamflow generation processes are not well constrained. GW-SW interactions and streamflow generation processes are further complicated by variability in precipitation inputs from summer and fall monsoon rains, as well as declines in snowpack and changing melt dynamics driven by warming temperatures. Using high spatial and temporal resolution radon and water stable isotope sampling and a 1D groundwater flux model, we evaluated how groundwater contributions and GW-SW interactions varied along a stream reach impacted by fractures (fractured-zone) and downstream of the fractured hillslope (non-fractured zone) in Coal Creek, a Colorado River headwater stream affected by summer monsoons. During early summer, groundwater contributions from the fractured zone were high, but declined throughout the summer. Groundwater contributions from the non-fractured zone were constant throughout the summer and became proportionally more important later in the summer. We hypothesize that groundwater in the non-fractured zone is dominantly sourced from a high-storage alluvial fan at the base of a tributary that is connected to Coal Creek throughout the summer and provides consistent groundwater influx. Water isotope data revealed that Coal Creek responds quickly to incoming precipitation early in the summer, and summer precipitation becomes more important for streamflow generation later in the summer. We quantified the change in catchment dynamic storage and found it negatively related to stream water isotope values, and positively related to modeled groundwater discharge and the ratio of fractured zone to non-fractured zone groundwater. We interpret these relationships as declining hydrologic connectivity throughout the summer leading to late summer streamflow supported predominantly by shallow flow paths, with variable response to drying from geologic features based on their storage. As groundwater becomes more important for sustaining summer flows, quantifying local geologic controls on groundwater inputs and their response to variable moisture conditions may become critical for accurate predictions of streamflow.
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- 2024
96. Life-long Learning and Testing for Automated Vehicles via Adaptive Scenario Sampling as A Continuous Optimization Process
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Ge, Jingwei, Wang, Pengbo, Chang, Cheng, Zhang, Yi, Yao, Danya, and Li, Li
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Computer Science - Robotics - Abstract
Sampling critical testing scenarios is an essential step in intelligence testing for Automated Vehicles (AVs). However, due to the lack of prior knowledge on the distribution of critical scenarios in sampling space, we can hardly efficiently find the critical scenarios or accurately evaluate the intelligence of AVs. To solve this problem, we formulate the testing as a continuous optimization process which iteratively generates potential critical scenarios and meanwhile evaluates these scenarios. A bi-level loop is proposed for such life-long learning and testing. In the outer loop, we iteratively learn space knowledge by evaluating AV in the already sampled scenarios and then sample new scenarios based on the retained knowledge. Outer loop stops when all generated samples cover the whole space. While to maximize the coverage of the space in each outer loop, we set an inner loop which receives newly generated samples in outer loop and outputs the updated positions of these samples. We assume that points in a small sphere-like subspace can be covered (or represented) by the point in the center of this sphere. Therefore, we can apply a multi-rounds heuristic strategy to move and pack these spheres in space to find the best covering solution. The simulation results show that faster and more accurate evaluation of AVs can be achieved with more critical scenarios.
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- 2024
97. Model-less Is the Best Model: Generating Pure Code Implementations to Replace On-Device DL Models
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Zhou, Mingyi, Gao, Xiang, Liu, Pei, Grundy, John, Chen, Chunyang, Chen, Xiao, and Li, Li
- Subjects
Computer Science - Software Engineering - Abstract
Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers to generate many kinds of attacks like adversarial attacks. Although securing deployed on-device DL models has gained increasing attention, no existing methods can fully prevent the aforementioned threats. Traditional software protection techniques have been widely explored, if on-device models can be implemented using pure code, such as C++, it will open the possibility of reusing existing software protection techniques. However, due to the complexity of DL models, there is no automatic method that can translate the DL models to pure code. To fill this gap, we propose a novel method, CustomDLCoder, to automatically extract the on-device model information and synthesize a customized executable program for a wide range of DL models. CustomDLCoder first parses the DL model, extracts its backend computing units, configures the computing units to a graph, and then generates customized code to implement and deploy the ML solution without explicit model representation. The synthesized program hides model information for DL deployment environments since it does not need to retain explicit model representation, preventing many attacks on the DL model. In addition, it improves ML performance because the customized code removes model parsing and preprocessing steps and only retains the data computing process. Our experimental results show that CustomDLCoder improves model security by disabling on-device model sniffing. Compared with the original on-device platform (i.e., TFLite), our method can accelerate model inference by 21.8% and 24.3% on x86-64 and ARM64 platforms, respectively. Most importantly, it can significantly reduce memory consumption by 68.8% and 36.0% on x86-64 and ARM64 platforms, respectively., Comment: Accepted by the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA2024)
- Published
- 2024
98. UV- and X-ray-activated broadband NIR garnet-type Ca3Ga2Sn3O12:Fe3+ phosphors with efficient persistent luminescence
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Wang, Yongjie, Bu, Qingzhou, Li, Dongshuo, Yang, Shuai, Li, Li, Xiang, Guotao, Jiang, Sha, Chang, Ying, Jing, Chuan, Zhou, Xianju, Bulyk, Lev-Ivan, and Suchocki, Andrzej
- Subjects
Condensed Matter - Materials Science - Abstract
Near-infrared phosphor-converted light-emitting diodes (NIR pc-LEDs) are compact light sources of great interest for NIR spectroscopy applications. Beyond typical Cr3+-activated NIR-emitting phosphors, there exists a strong demand for Cr3+-free alternatives with high efficiency and broadband emission to rich the landscape of NIR luminescent materials and extend their range of application fields. Here, we report a series of Fe3+-activated Ca3Ga2Sn3O12 garnet-type phosphors exhibiting broadband NIR emission in the 650-1000 nm range attributed to 4T1(G)-->6A1(S) transition, with a maximum at 754 nm and a FWHM of 89 nm upon UV excitation. The spectroscopic results were analyzed according to the Tanabe-Sugano theory from which the crystal field parameter Dq and Racah parameters B and C were obtained for the octahedrally coordinated Fe3+ ion. Notably, the NIR persistent luminescence lasting over 1 h was detected following UV or X-ray irradiation. The possible mechanism involving electron traps was proposed to explain the observed persistent luminescence. Furthermore, a NIR pc-LED was fabricated by coating synthesized phosphor on a UV chip, and its performance was evaluated to assess its potential suitability as a NIR light source. Our discovery of novel type of nontoxic Fe3+-activated broadband NIR luminescence phosphors with efficient NIR persistent luminescence paves the way for discovering Cr3+-free multifunctional NIR luminescence materials, thereby expanding their application possibilities., Comment: 18 pages, 5 fugures
- Published
- 2024
99. Luminescence properties and phase transformation of broadband NIR emitting A2(WO4)3:Cr3+ (A=Al3+, Sc3+) phosphors toward NIR spectroscopy applications
- Author
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Yang, Shuai, Wang, Yongjie, Xiang, Guotao, Jiang, Sha, Li, Li, Ling, Faling, Hu, Huanhuan, Zhang, Yuanyuan, Zhou, Xianju, and Suchocki, Andrzej
- Subjects
Condensed Matter - Materials Science - Abstract
The synthesis, structural, and luminescence properties have been carried out for Cr3+-activated Al2(WO4)3 (AWO) and Sc2(WO4)3 (SWO) phosphors for application in pc-NIR LED. Upon blue excitation, these compounds are capable of exhibiting broadband NIR emission stems primarily from 4T2-->4A2 transition in the range of 670-1200 nm (maxima ~808 nm, FWHM ~140 nm) for AWO:Cr and of 700-1300 nm (maxima ~870 nm, FWHM ~164 nm) for SWO:Cr. The significant shift of NIR emission is attributed to the substitution of AlO6 with larger ScO6 octahedrons. To gain insight into the luminescence the crystal field strength, Racah parameters, nephelauxetic effect, and electron-phonon coupling have been analyzed based on spectroscopic results. The electron-phonon coupling parameter S for SWO:Cr was determined to be 11.5, twice as large as that for AWO:Cr, which is in accordance with its strong thermal quenching. The abrupt changes occurring at 275 K in temperature-dependent luminescence spectra and decay lifetime of AWO:Cr is associated with temperature-driven phase transformation from low-temperature monoclinic to high-temperature orthorhombic phase. Pressure induced amorphization of AWO:Cr at pressures higher than 25 kbar was confirmed by employing high pressure evolution of Raman spectra. A high-power NIR pc-LED, fabricated by coating AWO:0.04Cr on a commercial 470 nm LED chip, shows good performance with an output power of 17.1 mW driven by a current of 320 mA, revealing potential application of studied materials for NIR light source., Comment: 19 pages, 7 figures
- Published
- 2024
- Full Text
- View/download PDF
100. Language Models Can Reduce Asymmetry in Information Markets
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Rahaman, Nasim, Weiss, Martin, Wüthrich, Manuel, Bengio, Yoshua, Li, Li Erran, Pal, Chris, and Schölkopf, Bernhard
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Computer Science and Game Theory ,Computer Science - Machine Learning ,Computer Science - Multiagent Systems ,Computer Science - Social and Information Networks - Abstract
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.
- Published
- 2024
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