51,685 results on '"Yang, Yu"'
Search Results
2. A Formation Control Deep Reinforcement Learning Model Based on MAPPO Algorithm for Unmanned Ground Vehicles
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Wang, Yiquan, primary, Yang, Yu, additional, Wang, Jingguo, additional, Li, Zhaodong, additional, and Zhao, Xijun, additional
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- 2024
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3. Utilizing Skip-Gram for Restaurant Vector Creation and Its Application in the Selection of Ideal Restaurant Locations
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Chang, Chih-Yung, primary, Jhang, Syu-Jhih, additional, Yang, Yu-Ting, additional, Chang, Hsiang-Chuan, additional, and Chang, Yun-Jui, additional
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- 2024
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4. Salt Precipitation Law of Formation Water During CO2 Injection into Depleted Gas Reservoirs
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Yang, Yu, primary, Xu, Qi-lin, additional, Jiang, Liang-wei, additional, Zhang, Qian, additional, Huang, Dong-jie, additional, Liu, Xin, additional, Liu, Rong-he, additional, Liu, Jian-guo, additional, and Cui, Yu-zhe, additional
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- 2024
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5. Task Allocation Algorithm for Multiple UAVs in IoT Networks
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Ye, Liang, primary, Yang, Yu, additional, Zhu, Rangang, additional, and Li, Xiaoshuai, additional
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- 2024
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6. SDN-Based Efficient Consortium Blockchain Network Architecture for Grid Information Authentication
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Liu, Tian, primary, Yang, Shuang, additional, Yang, Yu, additional, Yang, Kelin, additional, Li, Bo, additional, Chao, Cong, additional, and Sun, Bin, additional
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- 2024
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7. Network Transplanting for the Functionally Modular Architecture
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Zhang, Quanshi, primary, Cheng, Xu, additional, Wang, Xin, additional, Yang, Yu, additional, and Wu, Yingnian, additional
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- 2023
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8. Is a direct numerical simulation (DNS) of Navier-Stokes equations with small enough grid spacing and time-step definitely reliable/correct?
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Qin, Shejie, Yang, Yu, Huang, Yongxiang, Mei, Xinyu, Wang, Lipo, and Liao, Shijun
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Physics - Fluid Dynamics - Abstract
Traditionally, results given by the direct numerical simulation (DNS) of Navier-Stokes equations are widely regarded as reliable benchmark solutions of turbulence, as long as grid spacing is fine enough (i.e. less than the minimum Kolmogorov scale) and time-step is small enough, say, satisfying the Courant-Friedrichs-Lewy condition. Is this really true? In this paper a two-dimensional sustained turbulent Kolmogorov flow is investigated numerically by the two numerical methods with detailed comparisons: one is the traditional `direct numerical simulation' (DNS), the other is the `clean numerical simulation' (CNS). The results given by DNS are a kind of mixture of the false numerical noise and the true physical solution, which however are mostly at the same order of magnitude due to the butterfly-effect of chaos. On the contrary, the false numerical noise of the results given by CNS is much smaller than the true physical solution of turbulence in a long enough interval of time so that a CNS result is very close to the true physical solution and thus can be used as a benchmark solution. It is found that numerical noise as a kind of artificial tiny disturbances can lead to huge deviations at large scale on the two-dimensional Kolmogorov turbulence, not only quantitatively (even in statistics) but also qualitatively (such as symmetry of flow). Thus, fine enough spatial grid spacing with small enough time-step alone cannot guarantee the validity of the DNS: it is only a necessary condition but not sufficient. This finding might challenge some assumptions in investigation of turbulence. So, DNS results of a few sustained turbulent flows might have huge deviations on both of small and large scales from the true solution of Navier-Stokes equations even in statistics. Hopefully, CNS as a new tool to investigate turbulent flows more accurately than DNS could bring us some new discoveries., Comment: 27 pages, 18 figures
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- 2024
9. A theoretical perspective on the almost dark galaxy Nube: exploring the fuzzy dark matter model
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Yang, Yu-Ming, Bi, Xiao-Jun, and Yin, Peng-Fei
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Astrophysics - Cosmology and Nongalactic Astrophysics ,High Energy Physics - Phenomenology - Abstract
In recent astronomical observations, an almost dark galaxy, designated as Nube, has unveiled an intriguing anomaly in its stellar distribution. Specifically, Nube exhibits an exceptionally low central brightness, with the 2D half-light radius of its stars far exceeding the typical values found in dwarf galaxies, and even surpassing those observed in ultra-diffuse galaxies (UDGs). This phenomenon is difficult to explain within the framework of cold dark matter (CDM). Meanwhile, due to its ultralight particle mass, fuzzy dark matter (FDM) exhibits a de Broglie wavelength on the order of kiloparsecs under the typical velocities of galaxies. The interference between different modes of the FDM wave gives rise to fluctuations in the gravitational field, which can lead to the dynamical heating of stars within galaxies, resulting in an expansion of their spatial distribution. In this paper, we aim to interpret the anomalous stellar distribution observed in Nube as a consequence of the dynamical heating effect induced by FDM. Our findings suggest that a FDM particle mass around $1-2\times 10^{-23}$ eV can effectively account for this anomaly. And we propose that the FDM dynamical heating effect provides a new insight into understanding the formation of field UDGs., Comment: 10 pages, 3 figures
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- 2024
10. Dual-Camera Smooth Zoom on Mobile Phones
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Wu, Renlong, Zhang, Zhilu, Yang, Yu, and Zuo, Wangmeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user's zoom experience. In this work, we introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles with ground-truth collection. To address the issue, we suggest a data factory solution where continuous virtual cameras are assembled to generate DCSZ data by rendering reconstructed 3D models of the scene. In particular, we propose a novel dual-camera smooth zoom Gaussian Splatting (ZoomGS), where a camera-specific encoding is introduced to construct a specific 3D model for each virtual camera. With the proposed data factory, we construct a synthetic dataset for DCSZ, and we utilize it to fine-tune FI models. In addition, we collect real-world dual-zoom images without ground-truth for evaluation. Extensive experiments are conducted with multiple FI methods. The results show that the fine-tuned FI models achieve a significant performance improvement over the original ones on DCSZ task. The datasets, codes, and pre-trained models will be publicly available., Comment: 24
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- 2024
11. Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation
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Wen, Zhiyuan, Cao, Jiannong, Yang, Yu, Yang, Ruosong, and Liu, Shuaiqi
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. It is essential for providing personalized services in various applications of Human-Computer Interaction (HCI), such as AI-based mental therapy and companion robots for the elderly. Most recent studies analyze the dialog content for personality classification yet overlook two major concerns that hinder their performance. First, crucial implicit factors contained in conversation, such as emotions that reflect the speakers' personalities are ignored. Second, only focusing on the input dialog content disregards the semantic understanding of personality itself, which reduces the interpretability of the results. In this paper, we propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC. To utilize affectivity within dialog content for accurate personality recognition, we fine-tuned a pre-trained language model specifically for emotion recognition in conversations, facilitating real-time affective annotations for utterances. For interpretability of recognition results, we formulate personality recognition as an NLI problem by determining whether the textual description of personality labels is entailed by the dialog content. Extensive experiments on two daily conversation datasets suggest that Affective-NLI significantly outperforms (by 6%-7%) state-of-the-art approaches. Additionally, our Flow experiment demonstrates that Affective-NLI can accurately recognize the speaker's personality in the early stages of conversations by surpassing state-of-the-art methods with 22%-34%., Comment: Accepted by IEEE PerCom 2024
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- 2024
12. SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
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Yang, Yu, Mishra, Siddhartha, Chiang, Jeffrey N, and Mirzasoleiman, Baharan
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Despite the effectiveness of data selection for large language models (LLMs) during pretraining and instruction fine-tuning phases, improving data efficiency in supervised fine-tuning (SFT) for specialized domains poses significant challenges due to the complexity of fine-tuning data. To bridge this gap, we introduce an effective and scalable data selection method for SFT, SmallToLarge (S2L), which leverages training trajectories from small models to guide the data selection for larger models. We demonstrate through extensive experiments that S2L significantly improves data efficiency in SFT for mathematical problem-solving, reducing the training data to just 11% of the original MathInstruct dataset (Yue et al., 2023) to match full dataset performance while outperforming state-of-the-art data selection algorithms by an average of 4.7% across 6 in- and out-domain evaluation datasets. Remarkably, selecting only 50K data for SFT, S2L achieves a 32.7% accuracy on the most challenging MATH (Hendrycks et al., 2021) benchmark, improving Phi-2 (Li et al., 2023b) by 16.6%. In clinical text summarization on the MIMIC-III dataset (Johnson et al., 2016), S2L again outperforms training on the full dataset using only 50% of the data. Notably, S2L can perform data selection using a reference model 40x smaller than the target model, proportionally reducing the cost of data selection.
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- 2024
13. MVCAL: Multi View Clustering for Active Learning
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Fan, Yi, primary, Jiang, Biao, additional, Chen, Di, additional, and Yang, Yu-Bin, additional
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- 2023
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14. The Influence of Workplace Ostracism on Employees’ Unsafe Behavior in the Post-epidemic Era: A Conditional Process Model
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Li, Naiwen, primary and Yang, Yu, additional
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- 2023
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15. Impact of subway station upper span construction on existing railway tunnel structure
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Li, Silei, primary, Huang, Chunfu, additional, Yang, Yu, additional, and Tian, Hai, additional
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- 2023
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16. DeepDiscord: Dual Contrastive Coding for Transferable Time Series Anomaly Detection
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Li, Xin-Yi, primary, Zhong, Pei-Nan, additional, Chen, Di, additional, Zhang, Zhen-Dong, additional, and Yang, Yu-Bin, additional
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- 2023
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17. Dissipative stabilization of high-dimensional GHZ states for neutral atoms
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Zhao, Yue, Yang, Yu-Qing, Li, Weibin, and Shao, Xiao-Qiang
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Quantum Physics - Abstract
High-dimensional quantum entanglement characterizes the entanglement of quantum systems within a larger Hilbert space, introducing more intricate and complex correlations among the entangled particles' states. The high-dimensional Greenberger-Horne-Zeilinger (GHZ) state, symbolic of this type of entanglement, is of significant importance in various quantum information processing applications. This study proposes integrating a neutral atom platform with quantum reservoir engineering to generate a high-dimensional GHZ state deterministically. Leveraging the advantages of neutral atoms in a modified unconventional Rydberg pumping mechanism, combined with controlled dissipation, we achieve a three-dimensional GHZ state with a fidelity surpassing 99\% through multiple pump and dissipation cycles. This innovative approach paves the way for experimentally feasible, deterministic preparation of high-dimensional GHZ states in Rydberg atom systems, thereby advancing the capabilities of quantum information processing., Comment: Accepted by Applied Physics Letters, 7 pages, 5 figures
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- 2024
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18. Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene Understanding
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Yang, Yu-Qi, Guo, Yu-Xiao, and Liu, Yang
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Data diversity and abundance are essential for improving the performance and generalization of models in natural language processing and 2D vision. However, 3D vision domain suffers from the lack of 3D data, and simply combining multiple 3D datasets for pretraining a 3D backbone does not yield significant improvement, due to the domain discrepancies among different 3D datasets that impede effective feature learning. In this work, we identify the main sources of the domain discrepancies between 3D indoor scene datasets, and propose Swin3D++, an enhanced architecture based on Swin3D for efficient pretraining on multi-source 3D point clouds. Swin3D++ introduces domain-specific mechanisms to Swin3D's modules to address domain discrepancies and enhance the network capability on multi-source pretraining. Moreover, we devise a simple source-augmentation strategy to increase the pretraining data scale and facilitate supervised pretraining. We validate the effectiveness of our design, and demonstrate that Swin3D++ surpasses the state-of-the-art 3D pretraining methods on typical indoor scene understanding tasks. Our code and models will be released at https://github.com/microsoft/Swin3D, Comment: technical report
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- 2024
19. Phonon-lithium ion interactions: A case study of LiM(SeO3)2
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Ouyang, Runxin, Yang, Yu, Guan, Chaohong, and Zhu, Hong
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Condensed Matter - Materials Science - Abstract
Li ion diffusion is fundamentally a thermally activated ion hopping process. Recently, soft lattice, anharmonic phonon and paddlewheel mechanism have been proposed to potentially benefit the ion transport, while the understanding of vibrational couplings of mobile ion and anions is still limited but essential. Herein, we access the ionic conductivity, the stability and the lattice dynamics in LiM(SeO3)2 (M =Al, Ga, In, Sc, Y, and La) with two types of oxygen anions within LiO4 polyhedron, namely edge-shared and corner-shared, the prototype of which, LiGa(SeO3)2, has been experimentally synthesized. We studied in detail the anharmonic and harmonic phonon interactions, as well as couplings between vibrations of edge-bonded or corner-bonded anions in Li polyanions and Li ion diffusion. As M changing from Sc to La, anharmonic phonons increase alongside reduced activation energy for Li diffusion. Phonon modes involving edge-bonded oxygen anions contribute more to Li migration than corner-bonded oxygen anions, owing to greater atomic interactions between Li ions and edge-bonded anions. Thus, rather than the overall lattice softness, attentions shall be paid to reduce the frequency of the critical phonons contributing to Li ion diffusions as well as to increase the anharmonicity, for the design of Li ion superionic conductors for all-solid-state-batteries.
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- 2024
20. Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach
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Ju, Chengyi, Cao, Jiannong, Yang, Yu, Yang, Zhen-Qun, and Lee, Ho Man
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Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence - Abstract
In the era of modern education, addressing cross-school learner diversity is crucial, especially in personalized recommender systems for elective course selection. However, privacy concerns often limit cross-school data sharing, which hinders existing methods' ability to model sparse data and address heterogeneity effectively, ultimately leading to suboptimal recommendations. In response, we propose HFRec, a heterogeneity-aware hybrid federated recommender system designed for cross-school elective course recommendations. The proposed model constructs heterogeneous graphs for each school, incorporating various interactions and historical behaviors between students to integrate context and content information. We design an attention mechanism to capture heterogeneity-aware representations. Moreover, under a federated scheme, we train individual school-based models with adaptive learning settings to recommend tailored electives. Our HFRec model demonstrates its effectiveness in providing personalized elective recommendations while maintaining privacy, as it outperforms state-of-the-art models on both open-source and real-world datasets.
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- 2024
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21. Quantum multiparameter estimation enhanced by a topological phase transition
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Yang, Yu, Yuan, Haidong, and Li, Fuli
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Quantum Physics - Abstract
In quantum multiparameter estimation, multiple to-be-estimated parameters are encoded in a quantum dynamics system by a unitary evolution. As the parameters vary, the system may undergo a topological phase transition (TPT). In this paper, we investigate two SU(2) TPT models and propose the singular behavior of the quantum metric tensor around the TPT point as a tool for the simultaneous optimal estimation of multiple parameters. We find that the proposed TPT sensing protocol can achieve the same metrology performance as the quantum-control-enhanced one. Moreover, the probe state of the TPT sensing protocol is only the ground state of the Hamiltonian rather than the entangled state required in the control-enhanced one. In addition, an adaptive multiparameter estimation strategy is developed for updating the estimated values until the desired quantum Cram\'er-Rao bound is approached. Our work reinforces the connection between quantum multiparameter estimation and topology physics, with potential inspiration for quantum critical metrology., Comment: 16 pages, 6 figures, 1 table
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- 2024
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22. Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
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Li, Yicong, Sun, Xiangguo, Chen, Hongxu, Zhang, Sixiao, Yang, Yu, and Xu, Guandong
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Computer Science - Information Retrieval - Abstract
Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on three real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method., Comment: accepted by TKDE
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- 2024
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23. Quantum squeezing in a nonlinear mechanical oscillator
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Marti, Stefano, von Lüpke, Uwe, Joshi, Om, Yang, Yu, Bild, Marius, Omahen, Andraz, Chu, Yiwen, and Fadel, Matteo
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Quantum Physics - Abstract
Mechanical degrees of freedom are natural candidates for continuous-variable quantum information processing and bosonic quantum simulations. These applications, however, require the engineering of squeezing and nonlinearities in the quantum regime. Here we demonstrate ground state squeezing of a gigahertz-frequency mechanical resonator coupled to a superconducting qubit. This is achieved by parametrically driving the qubit, which results in an effective two-phonon drive. In addition, we show that the resonator mode inherits a nonlinearity from the off-resonant coupling with the qubit, which can be tuned by controlling the detuning. We thus realize a mechanical squeezed Kerr oscillator, where we demonstrate the preparation of non-Gaussian quantum states of motion with Wigner function negativities and high quantum Fisher information. This shows that our results also have applications in quantum metrology and sensing.
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- 2023
24. Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
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Wang, Zilin, Zhuang, Haolin, Li, Lu, Zhang, Yinmin, Zhong, Junjie, Chen, Jun, Yang, Yu, Tang, Boshi, and Wu, Zhiyong
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Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence ,I.3.7 - Abstract
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences because they lack exploration capabilities. The E3D2 framework involves a reward model trained from automatically-ranked dance demonstrations, which then guides the reinforcement learning process. This approach encourages the agent to explore and generate high quality and diverse dance movement sequences. The soundness of the reward model is both theoretically and experimentally validated. Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset. Project Page: https://sites.google.com/view/e3d2., Comment: AAAI-24
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- 2023
25. Constraints on Lorentz invariance violation from the LHAASO observation of GRB 221009A
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Yang, Yu-Ming, Bi, Xiao-Jun, and Yin, Peng-Fei
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
In some quantum gravity (QG) theories, Lorentz symmetry may be broken above the Planck scale. The Lorentz invariance violation (LIV) may induce observable effects at low energies and be detected at high energy astrophysical measurements. The Large High Altitude Air Shower Observatory(LHAASO) has detected the onset, rise, and decay phases of the afterglow of GRB 221009A, covering a wide energy range of photons approximately from $0.2$ to $18$ TeV. This observation provides an excellent opportunity to study the Lorentz invariance violation effect. In this study, we simultaneously utilize the data from the KM2A and WCDA detectors of LHAASO, and apply two event by event methods, namely the pair view method and maximum likelihood method, to investigate LIV. We obtain stringent constraints on the QG energy scale. For instance, through the maximum likelihood method, we determine the 95$\%$ confidence level lower limits to be $E_{QG,1} > 14.7 (6.5)\times 10^{19}$GeV for the subluminal (superluminal) scenario of $n = 1$, and $E_{QG,2} > 12.0 (7.2)\times 10^{11}$GeV for the subluminal (superluminal) scenario of $n = 2$. We find that the rapid rise and slow decay behaviors of the afterglow can impose strong constraints on the subluminal scenario, while the constraints are weaker for the superluminal scenario., Comment: 11 pages, 6 figures.Accepted for publication in JCAP
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- 2023
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26. Stability of Ecological Systems: A Theoretical Review
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Chen, Can, Wang, Xu-Wen, and Liu, Yang-Yu
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Mathematics - Dynamical Systems ,Electrical Engineering and Systems Science - Systems and Control - Abstract
The stability of ecological systems is a fundamental concept in ecology, which offers profound insights into species coexistence, biodiversity, and community persistence. In this article, we provide a systematic and comprehensive review on the theoretical frameworks for analyzing the stability of ecological systems. Notably, we survey various stability notions, including linear stability, sign stability, diagonal stability, D-stability, total stability, sector stability, structural stability, and higher-order stability. For each of these stability notions, we examine necessary or sufficient conditions for achieving such stability and demonstrate the intricate interplay of these conditions on the network structures of ecological systems. Finally, we explore the future prospects of these stability notions.
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- 2023
27. Camera-based 3D Semantic Scene Completion with Sparse Guidance Network
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Mei, Jianbiao, Yang, Yu, Wang, Mengmeng, Zhu, Junyu, Zhao, Xiangrui, Ra, Jongwon, Li, Laijian, and Liu, Yong
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving. Recently, many studies have turned to camera-based SSC solutions due to the richer visual cues and cost-effectiveness of cameras. However, existing methods usually rely on sophisticated and heavy 3D models to directly process the lifted 3D features that are not discriminative enough for clear segmentation boundaries. In this paper, we adopt the dense-sparse-dense design and propose an end-to-end camera-based SSC framework, termed SGN, to diffuse semantics from the semantic- and occupancy-aware seed voxels to the whole scene based on geometry prior and occupancy information. By designing hybrid guidance (sparse semantic and geometry guidance) and effective voxel aggregation for spatial occupancy and geometry priors, we enhance the feature separation between different categories and expedite the convergence of semantic diffusion. Extensive experimental results on the SemanticKITTI dataset demonstrate the superiority of our SGN over existing state-of-the-art methods.
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- 2023
28. Decoding Data Quality via Synthetic Corruptions: Embedding-guided Pruning of Code Data
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Yang, Yu, Singh, Aaditya K., Elhoushi, Mostafa, Mahmoud, Anas, Tirumala, Kushal, Gloeckle, Fabian, Rozière, Baptiste, Wu, Carole-Jean, Morcos, Ari S., and Ardalani, Newsha
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Code datasets, often collected from diverse and uncontrolled sources such as GitHub, potentially suffer from quality issues, thereby affecting the performance and training efficiency of Large Language Models (LLMs) optimized for code generation. Previous studies demonstrated the benefit of using embedding spaces for data pruning, but they mainly focused on duplicate removal or increasing variety, and in other modalities, such as images. Our work focuses on using embeddings to identify and remove "low-quality" code data. First, we explore features of "low-quality" code in embedding space, through the use of synthetic corruptions. Armed with this knowledge, we devise novel pruning metrics that operate in embedding space to identify and remove low-quality entries in the Stack dataset. We demonstrate the benefits of this synthetic corruption informed pruning (SCIP) approach on the well-established HumanEval and MBPP benchmarks, outperforming existing embedding-based methods. Importantly, we achieve up to a 3% performance improvement over no pruning, thereby showing the promise of insights from synthetic corruptions for data pruning., Comment: 12 pages, 4 figures, Oral Presentation at 3rd Workshop on Efficient Natural Language and Speech Processing (ENLSP-III), NeurIPS 2023
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- 2023
29. Determination of Toxicological Evaluation of Red Mold Rice Extract (ANKASCIN 568-R): Study about Chronic Toxicity and Genotoxicity
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Lin, Che-Wei, primary, Chen, Hsiao-Lin, additional, Yang, Yu-Hui, additional, Chen, Ya-Yuan, additional, Hsu, Ya-Wen, additional, and Pan, Tzu-Ming, additional
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- 2023
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30. Building a Curriculum to Foster Global Competence and Promote the Public Interest: Social Entrepreneurship and Digital Skills for American Community College Students
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Yang Yu and Faye Duchin
- Abstract
This paper proposes a new approach for combining top-down and bottom-up approaches intended to lead to a curriculum for action in the public interest that builds on social entrepreneurship and digital skills for students at community colleges. This integrated approach requires a collaborative, participatory approach and aims to provide relevant content for students with different cultures and backgrounds, personal values, and sense of identity. The objective is for all participants to engage in the learning process, become more confident, and develop contemporary skills that inspire and enable them to take initiatives to tackle global challenges and to thrive in a multicultural world. A relevant curriculum must enable students to understand the global and local situations in different geographies and, with the increasing demand for digital skills, to access and share information over networks, to develop possible solutions, and to make them happen. This paper proposes ideas for stimulating students to think about what they can do for the public good, starting with local issues, and to generate outcomes valued by the community. The ideas proposed for specific local communities in Maryland can be generalized for understanding and addressing problems for different communities in the United States as well.
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- 2024
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31. Highly efficient capture approach for the identification of diverse inherited retinal disorders.
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Kao, Hsiao-Jung, Lin, Ting-Yi, Hsieh, Feng-Jen, Chien, Jia-Ying, Yeh, Erh-Chan, Lin, Wan-Jia, Chen, Yi-Hua, Ding, Kai-Hsuan, Yang, Yu, Chi, Sheng-Chu, Tsai, Ping-Hsing, Hsu, Chih-Chien, Hwang, De-Kuang, Tsai, Hsien-Yang, Peng, Mei-Ling, Lee, Shi-Huang, Chau, Siu-Fung, Chen, Chen, Cheang, Wai-Man, Chen, Shih-Jen, Chiou, Shih-Hwa, Lu, Mei-Yeh, Huang, Shun-Ping, and Kwok, Pui-Yan
- Abstract
Our study presents a 319-gene panel targeting inherited retinal dystrophy (IRD) genes. Through a multi-center retrospective cohort study, we validated the assays effectiveness and clinical utility and characterized the mutation spectrum of Taiwanese IRD patients. Between January 2018 and May 2022, 493 patients in 425 unrelated families, all initially suspected of having IRD without prior genetic diagnoses, underwent detailed ophthalmic and physical examinations (with extra-ocular features recorded) and genetic testing with our customized panel. Disease-causing variants were identified by segregation analysis and clinical interpretation, with validation via Sanger sequencing. We achieved a read depth of >200× for 94.2% of the targeted 1.2 Mb region. 68.5% (291/425) of the probands received molecular diagnoses, with 53.9% (229/425) resolved cases. Retinitis pigmentosa (RP) is the most prevalent initial clinical impression (64.2%), and 90.8% of the cohort have the five most prevalent phenotypes (RP, cone-rod syndrome, Ushers syndrome, Lebers congenital amaurosis, Bietti crystalline dystrophy). The most commonly mutated genes of probands that received molecular diagnosis are USH2A (13.7% of the cohort), EYS (11.3%), CYP4V2 (4.8%), ABCA4 (4.5%), RPGR (3.4%), and RP1 (3.1%), collectively accounted for 40.8% of diagnoses. We identify 87 unique unreported variants previously not associated with IRD and refine clinical diagnoses for 21 patients (7.22% of positive cases). We developed a customized gene panel and tested it on the largest Taiwanese cohort, showing that it provides excellent coverage for diverse IRD phenotypes.
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- 2024
32. Role of the isospin diffusion on cluster transfer in $^{12,14}$C + $^{209}$Bi reactions
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Gao, Zepeng, Zhang, Yinu, Zhu, Long, Liao, Zehong, Yang, Yu, Guo, Chenchen, and Su, Jun
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Nuclear Theory - Abstract
Heavy-ion collisions at near-barrier energies provide a crucial pathway for investigating nucleon correlations and clustering structures. Recent experimental results showed that the valence neutrons in light projectiles obviously enhance the $\alpha$ transfer. This finding is extremely puzzled and fascinating, because it violates the ground-state $Q$ value systematics unexpectedly. In this work, the time-dependent Hartree-Fock approach is utilized to investigate the cluster transfer. By comparing the reactions $^{12,14}$C + $^{209}$Bi, we discover that above puzzling behavior is because of the strong correlation between isospin diffusion and clustering. Our calculations clearly show that the equilibrium of neutron-to-proton ratio strongly inhibits the clustering. This work opens a prospect for investigating the clustering in open quantum system.
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- 2023
33. Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
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Yang, Yu, Yang, Qihong, Deng, Yangtao, and He, Qiaolin
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Mathematics - Numerical Analysis ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In this work, we propose an end-to-end adaptive sampling neural network (MMPDE-Net) based on the moving mesh method, which can adaptively generate new sampling points by solving the moving mesh PDE. This model focuses on improving the quality of sampling points generation. Moreover, we develop an iterative algorithm based on MMPDE-Net, which makes the sampling points more precise and controllable. Since MMPDE-Net is a framework independent of the deep learning solver, we combine it with physics-informed neural networks (PINN) to propose moving sampling PINN (MS-PINN) and demonstrate its effectiveness by error analysis under some assumptions. Finally, we demonstrate the performance improvement of MS-PINN compared to PINN through numerical experiments of four typical examples, which numerically verify the effectiveness of our method.
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- 2023
34. Training A Multi-stage Deep Classifier with Feedback Signals
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Xu, Chao, Yang, Yu, Wang, Rongzhao, Wang, Guan, and Lin, Bojia
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons. The classifiers of a multi-stage process are usually Neural Network (NN) models trained independently or in their inference order without considering the signals from the latter stages. Aimed at two-stage binary classification process, the most common type of MSC, we propose a novel training framework, named Feedback Training. The classifiers are trained in an order reverse to their actual working order, and the classifier at the later stage is used to guide the training of initial-stage classifier via a sample weighting method. We experimentally show the efficacy of our proposed approach, and its great superiority under the scenario of few-shot training.
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- 2023
35. Boosting Summarization with Normalizing Flows and Aggressive Training
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Yang, Yu and Shen, Xiaotong
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization. Our approach tackles two primary challenges in variational summarization: insufficient semantic information in latent representations and posterior collapse during training. To address these challenges, we employ normalizing flows to enable flexible latent posterior modeling, and we propose a controlled alternate aggressive training (CAAT) strategy with an improved gate mechanism. Experimental results show that FlowSUM significantly enhances the quality of generated summaries and unleashes the potential for knowledge distillation with minimal impact on inference time. Furthermore, we investigate the issue of posterior collapse in normalizing flows and analyze how the summary quality is affected by the training strategy, gate initialization, and the type and number of normalizing flows used, offering valuable insights for future research.
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- 2023
36. Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
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Xia, Jiangnan, Yang, Yu, Wang, Senzhang, Yin, Hongzhi, Cao, Jiannong, and Yu, Philip S.
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Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence ,Computer Science - Social and Information Networks - Abstract
POI recommendation is practically important to facilitate various Location-Based Social Network services, and has attracted rising research attention recently. Existing works generally assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. However, in real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes including positioning error and user privacy concerns, leading to significant negative impacts on the performance of the POI recommendation. To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network. Specifically, we construct personal POI transition graph, the semantic-based POI graph and distance-based POI graph to comprehensively model the dependencies among the POIs. As the personal POI transition graph is usually sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency learning module for data augmentation from the local view. A Bayesian posterior guided graph augmentation approach is adopted to generate a new graph with collaborative signals to increase the data diversity. Then both the original and the augmented graphs are used for POI representation learning to counteract the data uncertainty issue. Next, the POI representations of the three view graphs are input into the proposed multi-view attention-based user preference learning module. By incorporating the semantic and distance correlations of POIs, the user preference can be effectively refined and finally robust recommendation results are achieved. The results of extensive experiments show that BayMAN significantly outperforms the state-of-the-art methods in POI recommendation when the available check-ins are incomplete and noisy.
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- 2023
37. Optimal Batched Best Arm Identification
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Jin, Tianyuan, Yang, Yu, Tang, Jing, Xiao, Xiaokui, and Xu, Pan
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Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs only in at most $3$ batches. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest., Comment: 32 pages, 1 figure, 3 tables
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- 2023
38. Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks
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Lee, Hao-Ping, Yang, Yu-Ju, von Davier, Thomas Serban, Forlizzi, Jodi, and Das, Sauvik
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Computer Science - Human-Computer Interaction - Abstract
Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.
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- 2023
39. Statistical Properties of X-Ray Bursts from SGR J1935+2154 Detected by Insight-HXMT
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Zhang, Wen-Long, Li, Xiu-Juan, Yang, Yu-Peng, Yi, Shuang-Xi, Li, Cheng-Kui, Tang, Qing-Wen, Qin, Ying, and Wang, Fa-Yin
- Subjects
Astrophysics - High Energy Astrophysical Phenomena - Abstract
As one class of the most important objects in the universe, magnetars can produce a lot of different frequency bursts including X-ray bursts. In \cite{2022ApJS..260...24C}, 75 X-ray bursts produced by magnetar SGR J1935+2154 during an active period in 2020 are published, including the duration and net photon counts of each burst, and waiting time based on the trigger time difference. In this paper, we utilize the power-law model, $dN(x)/dx\propto (x+x_0)^{-\alpha_x}$, to fit the cumulative distributions of these parameters. It can be found that all the cumulative distributions can be well fitted, which can be interpreted by a self-organizing criticality theory. Furthermore, we check whether this phenomenon still exist in different energy bands and find that there is no obvious evolution. These findings further confirm that the X-ray bursts from magnetars are likely to be generated by some self-organizing critical process, which can be explained by a possible magnetic reconnection scenario in magnetars., Comment: 6 pages, 1 figure and 3 tables; published in Research in Astronomy and Astrophysics
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- 2023
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40. Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
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Chen, Xuxi, Yang, Yu, Wang, Zhangyang, and Mirzasoleiman, Baharan
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a notable performance gap when compared to training on the original data. In this work, we are the first to argue that using just one synthetic subset for distillation will not yield optimal generalization performance. This is because the training dynamics of deep networks drastically change during the training. Hence, multiple synthetic subsets are required to capture the training dynamics at different phases of training. To address this issue, we propose Progressive Dataset Distillation (PDD). PDD synthesizes multiple small sets of synthetic images, each conditioned on the previous sets, and trains the model on the cumulative union of these subsets without requiring additional training time. Our extensive experiments show that PDD can effectively improve the performance of existing dataset distillation methods by up to 4.3%. In addition, our method for the first time enable generating considerably larger synthetic datasets., Comment: Preprint
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- 2023
41. On the Stability of Expressive Positional Encodings for Graphs
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Huang, Yinan, Lu, William, Robinson, Joshua, Yang, Yu, Zhang, Muhan, Jegelka, Stefanie, and Li, Pan
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Designing effective positional encodings for graphs is key to building powerful graph transformers and enhancing message-passing graph neural networks. Although widespread, using Laplacian eigenvectors as positional encodings faces two fundamental challenges: (1) \emph{Non-uniqueness}: there are many different eigendecompositions of the same Laplacian, and (2) \emph{Instability}: small perturbations to the Laplacian could result in completely different eigenspaces, leading to unpredictable changes in positional encoding. Despite many attempts to address non-uniqueness, most methods overlook stability, leading to poor generalization on unseen graph structures. We identify the cause of instability to be a ``hard partition'' of eigenspaces. Hence, we introduce Stable and Expressive Positional Encodings (SPE), an architecture for processing eigenvectors that uses eigenvalues to ``softly partition'' eigenspaces. SPE is the first architecture that is (1) provably stable, and (2) universally expressive for basis invariant functions whilst respecting all symmetries of eigenvectors. Besides guaranteed stability, we prove that SPE is at least as expressive as existing methods, and highly capable of counting graph structures. Finally, we evaluate the effectiveness of our method on molecular property prediction, and out-of-distribution generalization tasks, finding improved generalization compared to existing positional encoding methods. Our code is available at \url{https://github.com/Graph-COM/SPE}., Comment: ICLR 2023
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- 2023
42. Sieve: Multimodal Dataset Pruning Using Image Captioning Models
- Author
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Mahmoud, Anas, Elhoushi, Mostafa, Abbas, Amro, Yang, Yu, Ardalani, Newsha, Leather, Hugh, and Morcos, Ari
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. This underscores the critical need for dataset pruning, as the quality of these datasets is strongly correlated with the performance of VLMs on downstream tasks. Using CLIPScore from a pretrained model to only train models using highly-aligned samples is one of the most successful methods for pruning. We argue that this approach suffers from multiple limitations including: false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs. To bridge the gap between the limited diversity of generated captions and the high diversity of alternative text (alt-text), we estimate the semantic textual similarity in the embedding space of a language model pretrained on unlabeled text corpus. Using DataComp, a multimodal dataset filtering benchmark, when evaluating on 38 downstream tasks, our pruning approach, surpasses CLIPScore by 2.6\% and 1.7\% on medium and large scale respectively. In addition, on retrieval tasks, Sieve leads to a significant improvement of 2.7% and 4.5% on medium and large scale respectively., Comment: Accepted in CVPR 2024
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- 2023
43. Radio Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology
- Author
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Tian, Xiao, Li, Jia-Lun, Yi, Shuang-Xi, Yang, Yu-Peng, Hu, Jian-Ping, Qu, Yan-Kun, and Wang, Fa-Yin
- Subjects
Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
The plateau phase in the radio afterglows has been observed in very few gamma-ray bursts (GRBs), and 27 radio light curves with plateau phase were acquired from the published literature in this article. We obtain the related parameters of the radio plateau, such as temporal indexes during the plateau phase ($\alpha_1$ and $\alpha_2$), break time ($\Tbz$) and the corresponding radio flux ($F_{\rm b}$). The two parameter Dainotti relation between the break time of the plateau and the corresponding break luminosity ($\Lbz$) in radio band is $\Lbz \propto \Tbz^{-1.20\pm0.24}$. Including the isotropic energy $\Eiso$ and the peak energy $\Epi$, the three parameter correlations for the radio plateaus are written as $\Lbz \propto \Tbz^{-1.01 \pm 0.24} \Eiso^{0.18 \pm 0.09}$ and $\Lbz \propto \Tbz^{-1.18 \pm 0.27} \Epi^{0.05 \pm 0.28}$, respectively. The correlations are less consistent with that of X-ray and optical plateaus, implying that radio plateaus may have a different physical mechanism. The typical frequencies crossing the observational band may be a reasonable hypothesis that causes the breaks of the radio afterglows. We calibrate GRBs empirical luminosity correlations as standard candle for constraining cosmological parameters, and find that our samples can constrain the flat $\Lambda$CDM model well, while are not sensitive to non-flat ${\Lambda}$CDM model. By combining GRBs with other probes, such as SN and CMB, the constraints on cosmological parameters are $\om = 0.297\pm0.006$ for the flat ${\Lambda}$CDM model and $\om = 0.283\pm0.008$, $\oL = 0.711\pm0.006$ for the non-flat ${\Lambda}$CDM model, respectively., Comment: 16 pages, 6 figures and 6 tables, accepted for publication in ApJ
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- 2023
44. Non-Uniform Sampling Reconstruction for Symmetrical NMR Spectroscopy by Exploiting Inherent Symmetry
- Author
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Lin, Enping, Fang, Ze, Huang, Yuqing, Yang, Yu, and Chen, Zhong
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
Symmetrical NMR spectroscopy constitutes a vital branch of multidimensional NMR spectroscopy, providing a powerful tool for the structural elucidation of biological macromolecules. Non-Uniform Sampling (NUS) serves as an effective strategy for averting the prohibitive acquisition time of multidimensional NMR spectroscopy by only sampling a few points according to NUS sampling schedules and reconstructing missing points via algorithms. However, current sampling schedules are unable to maintain the accurate recovery of cross peaks that are weak but important. In this work, we propose a novel sampling schedule termed as SCPG (Symmetrical Copy Poisson Gap) and employ CS (Compressed Sensing) methods for reconstruction. We theoretically prove that the symmetrical constraint, apart from sparsity, is implicitly implemented when SCPG is combined with CS methods. The simulated and experimental data substantiate the advantage of SCPG over state-of-the-art 2D Woven PG in the NUS reconstruction of symmetrical NMR spectroscopy., Comment: 30 pages, 6 figures
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- 2023
45. Feasibility of Local Trajectory Planning for Level-2+ Semi-autonomous Driving without Absolute Localization
- Author
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Zhu, Sheng, Wang, Jiawei, Yang, Yu, and Aksun-Guvenc, Bilin
- Subjects
Computer Science - Robotics ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for level-2+ (L2+) semi-autonomous vehicles without the dependence on accurate absolute localization. Instead, we emphasize the estimation of the pose change between consecutive planning frames from motion sensors and integration of relative locations of traffic objects to the local planning problem under the ego car's local coordinate system, therefore eliminating the need for an absolute localization. Without the availability of absolute localization for correction, the measurement errors of speed and yaw rate greatly affect the estimation accuracy of the relative pose change between frames. We proved that the feasibility/stability of the continuous planning problem under such motion sensor errors can be guaranteed at certain defined conditions. This was achieved by formulating it as a Lyapunov-stability analysis problem. Moreover, a simulation pipeline was developed to further validate the proposed local planning method. Simulations were conducted at two traffic scenes with different error settings for speed and yaw rate measurements. The results substantiate the proposed framework's functionality even under relatively inferior sensor errors. We also experiment the stability limits of the planned results under abnormally larger motion sensor errors. The results provide a good match to the previous theoretical analysis. Our findings suggested that precise absolute localization may not be the sole path to achieving reliable trajectory planning, eliminating the necessity for high-accuracy dual-antenna GPS as well as the high-fidelity maps for SLAM localization., Comment: 11 pages, 13 figures, github url: https://github.com/codezs09/l2_frenet_planner
- Published
- 2023
46. A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach
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Zhuo, Lin, Cheng, Yinchu, Liu, Shaoqin, Yang, Yu, Tang, Shuang, Zhen, Jiancun, Zhao, Junfeng, and Zhan, Siyan
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
BackgroundThe inappropriate use of prescription medication has recently garnered worldwide attention, but most national policies do not effectively provide for early detection or timely intervention. ObjectiveThis study aimed to develop and assess the validity of a model that can detect the inappropriate use of prescription medication. This effort combines a multiview and topic matching method. The study also assessed the validity of this approach. MethodsA multiview extension of the latent Dirichlet allocation algorithm for topic modeling was chosen to generate diagnosis-medication topics, with data obtained from the Chinese Monitoring Network for Rational Use of Drugs (CMNRUD) database. Topic mapping allowed for calculating the degree to which diagnoses and medications were similarly distributed and, by setting a threshold, for identifying prescription misuse. The Beijing Regional Prescription Review Database (BRPRD) database was used as the gold standard to assess the model’s validity. We also conducted a sensitivity analysis using random samples of validated prescriptions and evaluated the model’s performance. ResultsA total of 44 million prescriptions were used to generate topics using the diagnoses and medications from the CMNRUD database. A random sample (15,000 prescriptions) from the BRPRD was used for validation, and it was found that the model had a sensitivity of 81.8%, specificity of 47.4%, positive-predictive value of 14.5%, and negative-predictive value of 96.0%. The model showed superior stability under different sampling proportions. ConclusionsA method that combines multiview topic modeling and topic matching can detect the inappropriate use of prescription medication. This model, which has mediocre specificity and moderate sensitivity, can be used as a primary screening tool and will likely complement and improve the process of manually reviewing prescriptions.
- Published
- 2020
- Full Text
- View/download PDF
47. Active Surveillance of Adverse Events Following Human Papillomavirus Vaccination: Feasibility Pilot Study Based on the Regional Health Care Information Platform in the City of Ningbo, China
- Author
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Liu, Zhike, Zhang, Liang, Yang, Yu, Meng, Ruogu, Fang, Ting, Dong, Ying, Li, Ning, Xu, Guozhang, and Zhan, Siyan
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundComprehensive safety data for vaccines from post-licensure surveillance, especially active surveillance, could guide administrations and individuals to make reasonable decisions on vaccination. Therefore, we designed a pilot study to assess the capability of a regional health care information platform to actively monitor the safety of a newly licensed vaccine. ObjectiveThis study aimed to conduct active surveillance of human papillomavirus (HPV) vaccine safety based on this information platform. MethodsIn 2017, one of China’s most mature information platforms with superior data linkage was selected. A structured questionnaire and open-ended interview guidelines were developed to investigate the feasibility of active surveillance following HPV vaccination using the regional health care information platform in Ningbo. The questionnaire was sent to participants via email, and a face-to-face interview was conducted to confirm details or resolve discrepancies. ResultsFive databases that could be considered essential to active surveillance of vaccine safety were integrated into the platform starting in 2015. Except for residents' health records, which had a coverage rate of 87%, the data sources covered more than 95% of the records that were documented in Ningbo. All the data could be inherently linked using the national identity card. There were 19,328 women who received the HPV vaccine, and 37,988 doses were administered in 2017 and 2018. Women aged 30-40 years accounted for the largest proportion. Quadrivalent vaccination accounted for 73.1% of total vaccination, a much higher proportion than that of bivalent vaccination. Of the first doses, 60 (60/19,328, 0.31%) occurred outside Ningbo. There were no missing data for vaccination-relevant variables, such as identity card, vaccine name, vaccination doses, vaccination date, and manufacturer. ICD-10 coding could be used to identify 9,180 cases using a predefined list of the outcomes of interest, and 1.88% of these cases were missing the identity card. During the 90 days following HPV vaccination, 4 incident cases were found through the linked vaccination history and electronic medical records. The combined incident rate of rheumatoid arthritis, optic neuritis, and Henoch-Schonlein purpura was 8.84/100,000 doses of bivalent HPV, and the incidence rate of rheumatoid arthritis was 3.75/100,000 doses of quadrivalent HPV. ConclusionsThis study presents an available approach to initiate an active surveillance system for adverse events following HPV vaccination, based on a regional health care information platform in China. An extended observation period or the inclusion of additional functional sites is warranted to conduct future hypothesis-generating and hypothesis-confirming studies for vaccine safety concerns.
- Published
- 2020
- Full Text
- View/download PDF
48. Signatures of the Self-organized Criticality Phenomenon in Precursors of Gamma-ray bursts
- Author
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Li, Xiu-Juan and Yang, Yu-Peng
- Subjects
Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
Precursors provide important clues to the nature of gamma-ray burst (GRB) central engines and can be used to contain GRB physical processes. In this letter, we study the self-organized criticality in precursors of long GRBs in the third Swift/BAT Catalog. We investigate the differential and cumulative size distributions of 100 precursors, including peak flux, duration, rise time, decay time, and quiescent time with the Markov Chain Monte Carlo technique. It is found that all of the distributions can be well described by power-law models and understood within the physical framework of a self-organized criticality system. In addition, we inspect the cumulative distribution functions of the size differences with a q-Gaussian function. The scale-invariance structures of precursors further strengthen our findings. Particularly, similar analyses are made in 127 main bursts. The results show that both precursors and main bursts can be attributed to an self-organized criticality system with the spatial dimension S = 3 and driven by the similar magnetically dominated process., Comment: Accepted for publication in ApJL
- Published
- 2023
49. Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
- Author
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Meng, Chang, Zhai, Chenhao, Yang, Yu, Zhang, Hengyu, and Li, Xiu
- Subjects
Computer Science - Information Retrieval - Abstract
Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation frameworks contain two steps: fusion and prediction. In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors. In the prediction step, multiple signals are utilized to jointly optimize the model with a multi-task learning (MTL) paradigm. However, recent approaches have not addressed the issue caused by imbalanced data distribution in the fusion step, resulting in the learned relationships being dominated by high-frequency behaviors. In the prediction step, the existing methods use a gate mechanism to directly aggregate expert information generated by coupling input, leading to negative information transfer. To tackle these issues, we propose a Parallel Knowledge Enhancement Framework (PKEF) for multi-behavior recommendation. Specifically, we enhance the hierarchical information propagation in the fusion step using parallel knowledge (PKF). Meanwhile, in the prediction step, we decouple the representations to generate expert information and introduce a projection mechanism during aggregation to eliminate gradient conflicts and alleviate negative transfer (PME). We conduct comprehensive experiments on three real-world datasets to validate the effectiveness of our model. The results further demonstrate the rationality and effectiveness of the designed PKF and PME modules. The source code and datasets are available at https://github.com/MC-CV/PKEF., Comment: Accepted by CIKM 2023
- Published
- 2023
- Full Text
- View/download PDF
50. Doubly heterogeneous networks facilitate the emergence of collective cooperation
- Author
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Meng, Yao, Cornelius, Sean P., Liu, Yang-Yu, and Li, Aming
- Subjects
Physics - Physics and Society ,Mathematics - Dynamical Systems - Abstract
There is growing recognition that the network structures arising from interactions between different entities in physical, social and biological systems fundamentally alter the evolutionary outcomes. Previous paradigm exploring evolutionary game dynamics has assumed that individuals update their strategies at an identical rate, reporting that structurally heterogeneous networks -- despite their ubiquity in real systems -- generally hinder the emergence of collective cooperation compared to their homogeneous counterparts. Here we solve this paradox by creating a new paradigm where individuals on arbitrary networks are allowed to update strategies at arbitrary, personalized rates, and provide the precise condition under which universal collective cooperation is favored. We find that when individuals' update rates vary inversely with their number of connections, heterogeneous networks actually outperform homogeneous ones in promoting cooperation. This surprising property of such "doubly heterogeneous" networks cautions against the conventional wisdom that heterogeneous networks are antagonistic to cooperation. We further develop an efficient protocol for optimizing the promotion of cooperation by tuning individuals' update rates in any structure. Our findings highlight that personalized interaction dynamics, beyond structure, in complex networks are fundamental to understanding and promoting collective cooperation., Comment: 10 pages, 5 figures
- Published
- 2023
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