46,053 results on '"Zhao, Wei"'
Search Results
2. A Compact Magnet System for the Tsinghua Tabletop Kibble Balance
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Ma, Yongchao, Li, Nanjia, Liu, Weibo, Ma, Kang, Zhao, Wei, Huang, Songling, and Li, Shisong
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Physics - Instrumentation and Detectors - Abstract
Although the so-called magnetic geometrical factor, $Bl$, of a Kibble balance does not appear in the Kibble equations, it offers the precision link between electrical and mechanical quantities and furthers a quasi-quantum traceability path for mass metrology. This feature makes the magnet system, supplying the $Bl$ in Kibble equations, play a core role in Kibble balances. Following the open-hardware idea, we report here on the design, manufacture, assembly, optimization, and finally performance of a compact magnet system for the Tsinghua tabletop Kibble balance. Notably, the magnet system showcased in this study facilitates a straightforward upper levitation of splitting through a streamlined mechanism guide, substantially enhancing the ease of open and close operations. Experimental tests show the realized magnet systems can yield a high $Bl$ value (e.g., 400 Tm for a bifilar coil and 800 Tm for a single coil with a wire gauge of 0.2 mm) meanwhile a low volume/weight (40 kg) thanks to the uniformity improvement of magnetic profiles. Furthermore, important parameters related to systematic effects, such as the current effect, are checked, aiming for a final mass-realization accuracy at the $10^{-8}$ level., Comment: 12 figures, submitted to IEEE Trans. I & M
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- 2024
3. On Mechanism Underlying Algorithmic Collusion
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Xu, Zhang and Zhao, Wei
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Economics - Theoretical Economics ,Computer Science - Computer Science and Game Theory ,Computer Science - Multiagent Systems - Abstract
Two issues of algorithmic collusion are addressed in this paper. First, we show that in a general class of symmetric games, including Prisoner's Dilemma, Bertrand competition, and any (nonlinear) mixture of first and second price auction, only (strict) Nash Equilibrium (NE) is stochastically stable. Therefore, the tacit collusion is driven by failure to learn NE due to insufficient learning, instead of learning some strategies to sustain collusive outcomes. Second, we study how algorithms adapt to collusion in real simulations with insufficient learning. Extensive explorations in early stages and discount factors inflates the Q-value, which interrupts the sequential and alternative price undercut and leads to bilateral rebound. The process is iterated, making the price curves like Edgeworth cycles. When both exploration rate and Q-value decrease, algorithms may bilaterally rebound to relatively high common price level by coincidence, and then get stuck. Finally, we accommodate our reasoning to simulation outcomes in the literature, including optimistic initialization, market design and algorithm design.
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- 2024
4. YOLOO: You Only Learn from Others Once
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Gu, Lipeng, Wei, Mingqiang, Yan, Xuefeng, Zhu, Dingkun, Zhao, Wei, Xie, Haoran, and Liu, Yong-Jin
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modalities only during training to avoid multi-modal input in the inference phase? To answer it, we propose \textbf{YOLOO}, a novel multi-modal 3D MOT paradigm: You Only Learn from Others Once. YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once. Leveraging this UTR, YOLOO achieves efficient tracking solely using the point cloud encoder without compromising its performance, fundamentally obviating the need for computationally intensive DNNs. Specifically, YOLOO includes two core components: a unified tri-modal encoder (UTEnc) and a flexible geometric constraint (F-GC) module. UTEnc integrates a point cloud encoder with image and text encoders adapted from pre-trained CLIP. It seamlessly fuses point cloud information with rich visual-textual knowledge from CLIP into the point cloud encoder, yielding highly discriminative UTRs that facilitate the association between trajectories and detections. Additionally, F-GC filters out mismatched associations with similar representations but significant positional discrepancies. It further enhances the robustness of UTRs without requiring any scene-specific tuning, addressing a key limitation of customized geometric constraints (e.g., 3D IoU). Lastly, high-quality 3D trajectories are generated by a traditional data association component. By integrating these advancements into a multi-modal 3D MOT scheme, our YOLOO achieves substantial gains in both robustness and efficiency.
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- 2024
5. A Bi-polar Current Source with High Short-term Stability for Tsinghua Tabletop Kibble Balance
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Ma, Kang, Liu, Xiaohu, Zhao, Wei, Huang, Songling, and Li, Shisong
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Physics - Instrumentation and Detectors ,Physics - Applied Physics - Abstract
A high-precision current source, capable of supporting weighing measurements with a relative uncertainty at the $10^{-9}$ level, is essential for Kibble balance experiments. However, most current sources utilized in Kibble balances to date are homemade and not commercially available. In this paper, we introduce a digital-feedback, two-stage current source designed for the Tsinghua tabletop Kibble balance, relying solely on commercially available sources and voltmeters. A high-resolution, small-range current source is employed to digitally compensate for current output fluctuations from a large-range current source. Experimental tests show the proposal can offer an easy realization of a current source with nA/A stability to support Kibble balance measurements., Comment: 10 figures, submitted to IEEE Trans. Instrum. Meas
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- 2024
6. CMD: A Cache-assisted GPU Memory Deduplication Architecture
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Zhao, Wei, Feng, Dan, Tong, Wei, Wei, Xueliang, and Wu, Bing
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Computer Science - Hardware Architecture - Abstract
Massive off-chip accesses in GPUs are the main performance bottleneck, and we divided these accesses into three types: (1) Write, (2) Data-Read, and (3) Read-Only. Besides, We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the inter-dup and inter-dup lines. As for the inter-dup detection, we reduce the extra read requests caused by the traditional read-verify hash process. Besides, we design several techniques to manage duplicate blocks. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 37.79%. Besides, CMD can improve the performance of memory-intensive workloads by 50.18%.
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- 2024
7. Magnetic Field of the Quasar 1604+159 from Parsec to Kilo-parsec Scale
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Hu, Xu-Zhi, Hong, Xiaoyu, Zhao, Wei, Chen, Liang, Wang, Wei-Yang, and Wu, Linhui
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
We present a multi-frequency polarimetric study for the quasar 1604+159. The source was observed at the $L$ band with the American Very Long Baseline Array (VLBA) and the $L$, $X$, and $U$ bands with the Very Large Array (VLA). These observations provide different resolutions from mas to arcsec, enabling us to probe the morphology and magnetic field from tens of parsec to hundreds of kilo-parsec scale. We detect a symmetrical Fanaroff-Riley-Class-I-like structure. The source has several lobes and bulges, forming a cocoon shape. The polarization is normal to the edges of the structure with high fractional polarization up to $\sim 60\%$. Two hotspots are observed at the eastern and western sides of the source, located symmetrically relative to the core. The flux density ratio ($>1.5$) between the two hotspots suggests the Doppler beaming effect exists at a large scale. The polarized emission in the hotspots also shows a symmetrical structure with an oblique direction from the jet direction. In general, the jet propagates in a collimating structure with several bends. Polarization is also detected perpendicular to the local jet from $\sim$100 mas to $\sim$ 1 arcsec. The jet shows strong polarized intensity and high fractional polarization at the bending edges. We discuss the possible origins of the observed structure and magnetic field., Comment: 17 pages, accepted for publication in ApJ
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- 2024
8. Aligning Multiple Knowledge Graphs in a Single Pass
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Yang, Yaming, Wang, Zhe, Guan, Ziyu, Zhao, Wei, Lu, Weigang, and Huang, Xinyan
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Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, and to the best of our knowledge, no existing EA method considers aligning multiple (more than two) KGs. To fill this research gap, in this work, we study a novel problem of aligning multiple KGs and propose an effective framework named MultiEA to solve the problem. First, we embed the entities of all the candidate KGs into a common feature space by a shared KG encoder. Then, we explore three alignment strategies to minimize the distances among pre-aligned entities. In particular, we propose an innovative inference enhancement technique to improve the alignment performance by incorporating high-order similarities. Finally, to verify the effectiveness of MultiEA, we construct two new real-world benchmark datasets and conduct extensive experiments on them. The results show that our MultiEA can effectively and efficiently align multiple KGs in a single pass.
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- 2024
9. A discussion on the critical electric Rayleigh number for AC electrokinetic flow of binary fluids in a divergent microchannel
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Pang, Jinan, Han, Yu, Sun, Bo, and Zhao, Wei
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Physics - Fluid Dynamics - Abstract
Electrokinetic (EK) flow is a type of flow driven or manipulated by electric body forces, influenced by various factors such as electric field intensity, electric field form, frequency, electric permittivity/conductivity, fluid viscosity and etc. The diversity of dimensionless control parameters, such as the electric Rayleigh number, complicates the comparison of EK flow stability. Consequently, comparing the performance and cost of micromixers or reactors based on EK flow is challenging, posing an obstacle to their industrial and engineering applications. In this investigation, we theoretically derived a new electric Rayleigh number ($Ra_e$) that quantifies the relationship among electric body forces, fluid viscosity, and ion diffusivity, based on a tanh model of electric conductivity distribution. The calculation results indicate that the new $Ra_e$ exhibits richer variation with the control parameters and better consistency with previous experimental reports. We further conducted experimental studies on the critical electric Rayleigh number ($Ra_{ec}$) of AC EK flow of binary fluids in a divergent microchannel. The experimental variations align well with the theoretical predictions, particularly the existence of an optimal AC frequency and electric conductivity ratio, demonstrating that the tanh model can better elucidate the underlying physics of EK flow. With the new electric Rayleigh number, we found that EK flow in the designed divergent microchannel has a much smaller $Ra_{ec}$ than previously reported, indicating that EK flow is more unstable and thus more suitable for applications in micromixers or reactors in industry and engineering.
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- 2024
10. Arbitrary quantum states preparation aided by deep reinforcement learning
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Wang, Zhao-Wei and Wang, Zhao-Ming
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Quantum Physics - Abstract
The preparation of quantum states is essential in the realm of quantum information processing, and the development of efficient methodologies can significantly alleviate the strain on quantum resources. Within the framework of deep reinforcement learning (DRL), we integrate the initial and the target state information within the state preparation task together, so as to realize the control trajectory design between two arbitrary quantum states. Utilizing a semiconductor double quantum dots (DQDs) model, our results demonstrate that the resulting control trajectories can effectively achieve arbitrary quantum state preparation (AQSP) for both single-qubit and two-qubit systems, with average fidelities of 0.9868 and 0.9556 for the test sets, respectively. Furthermore, we consider the noise around the system and the control trajectories exhibit commendable robustness against charge and nuclear noise. Our study not only substantiates the efficacy of DRL in QSP, but also provides a new solution for quantum control tasks of multi-initial and multi-objective states, and is expected to be extended to a wider range of quantum control problems.
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- 2024
11. Performance analysis for a rotary compressor at high speed: experimental study and mathematical modeling
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Zheng, Chuntai, Zhao, Wei, Lyu, Benshuai, Gao, Keke, Cao, Hongjun, Zhong, Lei, Gao, Yi, and Liao, Ren
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Electrical Engineering and Systems Science - Signal Processing - Abstract
This paper conducted a comprehensive study on the performance of a rotary compressor over a rotational speed range of 80Hz to 200Hz through experimental tests and mathematical modeling. A compressor performance test rig was designed to conduct the performance tests, with fast-response pressure sensors and displacement sensors capturing the P-V diagram and dynamic motion of the moving components. Results show that the compressor efficiency degrades at high speeds due to the dominant loss factors of leakage and discharge power loss. Supercharging effects become significant at speeds above 160Hz, and its net effects reduce the compressor efficiency, especially at high speeds. This study identifies and analyzes the loss factors on the mass flow rate and power consumption based on experimental data, and hypothesizes possible mechanisms for each loss factor, which can aid in the design of a high-speed rotary compressor with higher efficiency.
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- 2024
12. Exploring Sound Change Over Time: A Review of Computational and Human Perception
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He, Siqi and Zhao, Wei
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Computer Science - Computation and Language - Abstract
Computational and human perception are often considered separate approaches for studying sound changes over time; few works have touched on the intersection of both. To fill this research gap, we provide a pioneering review contrasting computational with human perception from the perspectives of methods and tasks. Overall, computational approaches rely on computer-driven models to perceive historical sound changes on etymological datasets, while human approaches use listener-driven models to perceive ongoing sound changes on recording corpora. Despite their differences, both approaches complement each other on phonetic and acoustic levels, showing the potential to achieve a more comprehensive perception of sound change. Moreover, we call for a comparative study on the datasets used by both approaches to investigate the influence of historical sound changes on ongoing changes. Lastly, we discuss the applications of sound change in computational linguistics, and point out that perceiving sound change alone is insufficient, as many processes of language change are complex, with entangled changes at syntactic, semantic, and phonetic levels., Comment: LChange24 Camera Ready
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- 2024
13. Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges
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Çelikkol, Melis, Körber, Lydia, and Zhao, Wei
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Computer Science - Computation and Language - Abstract
Everlasting contact between language communities leads to constant changes in languages over time, and gives rise to language varieties and dialects. However, the communities speaking non-standard language are often overlooked by non-inclusive NLP technologies. Recently, there has been a surge of interest in studying diatopic and diachronic changes in dialect NLP, but there is currently no research exploring the intersection of both. Our work aims to fill this gap by systematically reviewing diachronic and diatopic papers from a unified perspective. In this work, we critically assess nine tasks and datasets across five dialects from three language families (Slavic, Romance, and Germanic) in both spoken and written modalities. The tasks covered are diverse, including corpus construction, dialect distance estimation, and dialect geolocation prediction, among others. Moreover, we outline five open challenges regarding changes in dialect use over time, the reliability of dialect datasets, the importance of speaker characteristics, limited coverage of dialects, and ethical considerations in data collection. We hope that our work sheds light on future research towards inclusive computational methods and datasets for language varieties and dialects., Comment: LChange24 Camera Ready
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- 2024
14. Caecal microbiome and metabolites associated with different growth performances of broilers
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Chen, Xue, Zhao, Wei, Liu, Yang-Zhi, Aschalew, Natnael D., Sun, Zhe, Zhang, Xue-Feng, Wang, Tao, Zhen, Yu-Guo, and Gui-Xin-Qin
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- 2021
- Full Text
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15. Triggering Receptor Expressed on Myeloid Cells 2 Alleviated Sevoflurane-Induced Developmental Neurotoxicity via Microglial Pruning of Dendritic Spines in the CA1 Region of the Hippocampus
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Deng, Li, Song, Shao-Yong, Zhao, Wei-Ming, Meng, Xiao-Wen, Liu, Hong, Zheng, Qing, Peng, Ke, and Ji, Fu-Hai
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Biological Psychology ,Biomedical and Clinical Sciences ,Neurosciences ,Psychology ,Pediatric ,Behavioral and Social Science ,Neurological ,CA1 neurons ,Dendritic spines ,Developmental neurotoxicity ,Microglia ,Sevoflurane ,TREM2 ,Cognitive Sciences ,Neurology & Neurosurgery ,Biological psychology - Abstract
Sevoflurane induces developmental neurotoxicity in mice; however, the underlying mechanisms remain unclear. Triggering receptor expressed on myeloid cells 2 (TREM2) is essential for microglia-mediated synaptic refinement during the early stages of brain development. We explored the effects of TREM2 on dendritic spine pruning during sevoflurane-induced developmental neurotoxicity in mice. Mice were anaesthetized with sevoflurane on postnatal days 6, 8, and 10. Behavioral performance was assessed using the open field test and Morris water maze test. Genetic knockdown of TREM2 and overexpression of TREM2 by stereotaxic injection were used for mechanistic experiments. Western blotting, immunofluorescence, electron microscopy, three-dimensional reconstruction, Golgi staining, and whole-cell patch-clamp recordings were performed. Sevoflurane exposures upregulated the protein expression of TREM2, increased microglia-mediated pruning of dendritic spines, and reduced synaptic multiplicity and excitability of CA1 neurons. TREM2 genetic knockdown significantly decreased dendritic spine pruning, and partially aggravated neuronal morphological abnormalities and cognitive impairments in sevoflurane-treated mice. In contrast, TREM2 overexpression enhanced microglia-mediated pruning of dendritic spines and rescued neuronal morphological abnormalities and cognitive dysfunction. TREM2 exerts a protective role against neurocognitive impairments in mice after neonatal exposures to sevoflurane by enhancing microglia-mediated pruning of dendritic spines in CA1 neurons. This provides a potential therapeutic target in the prevention of sevoflurane-induced developmental neurotoxicity.
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- 2024
16. Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
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Quan, Wuzhou, Zhao, Wei, Wang, Weiming, Xie, Haoran, Wang, Fu Lee, and Wei, Mingqiang
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images. We propose HintU, a novel network to recover the local features lost by various UNet-based methods for effective infrared small target detection. HintU has two key contributions. First, it introduces the "Hint" mechanism for the first time, i.e., leveraging the prior knowledge of target locations to highlight critical local features. Second, it improves the mainstream UNet-based architecture to preserve target pixels even after downsampling. HintU can shift the focus of various networks (e.g., vanilla UNet, UNet++, UIUNet, MiM+, and HCFNet) from the irrelevant background pixels to a more restricted area from the beginning. Experimental results on three datasets NUDT-SIRST, SIRSTv2 and IRSTD1K demonstrate that HintU enhances the performance of existing methods with only an additional 1.88 ms cost (on RTX Titan). Additionally, the explicit constraints of HintU enhance the generalization ability of UNet-based methods. Code is available at https://github.com/Wuzhou-Quan/HintU.
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- 2024
17. Structure of Massive Gauge/Gravity Scattering Amplitudes, Equivalence Theorems, and Extended Double-Copy with Compactified Warped Space
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Hang, Yanfeng, Zhao, Wei-Wei, He, Hong-Jian, and Qiu, Yin-Long
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High Energy Physics - Theory ,General Relativity and Quantum Cosmology ,High Energy Physics - Phenomenology - Abstract
We study the structure of scattering amplitudes of massive Kaluza-Klein (KK) states in the compactified 5-dimensional warped gauge and gravity theories. We present systematic formulations of the gauge theory equivalence theorem (GAET) and the gravitational equivalence theorem (GRET) for warped KK theories in $R_\xi^{}$ gauge, where the GAET connects the scattering amplitudes of longitudinal KK gauge bosons to that of the corresponding KK Goldstone bosons and the GRET connects the scattering amplitudes of KK gravitons of helicity-zero (helicity-one) to that of the corresponding gravitational KK Goldstone bosons. We analyze the structure of 3-point and 4-point scattering amplitudes of massive KK gauge bosons and of massive KK gravitons as well as their corresponding Goldstone bosons. We first prove the GAET and GRET explicitly for the fundamental 3-point KK gauge/gravity scattering amplitudes. We then demonstrate that the validity of the GAET and GRET for 4-point gauge/gravity scattering amplitudes can be reduced to the validity of GAET and GRET for 3-point gauge/gravity scattering amplitudes at tree level. With these, we study the double-copy construction of KK scattering amplitudes in the warped gauge/gravity theories. We newly realize the double-copy for massive 3-point full gauge/gravity amplitudes at tree level under proper correspondences of color-kinematics and of gauge/gravity couplings, whereas we can construct the double-copy for 4-point KK gauge/gravity amplitudes to the leading order (LO) of high energy expansion. We further demonstrate that this LO double-copy construction can be extended to $N$-point KK scattering amplitudes with $N\geqslant 4$., Comment: 91 pages. Improved version, references added
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- 2024
18. Enhancing Criminal Case Matching through Diverse Legal Factors
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Zhao, Jie, Guan, Ziyu, Zhao, Wei, and Jiang, Yue
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Computer Science - Computation and Language - Abstract
Criminal case matching endeavors to determine the relevance between different criminal cases. Conventional methods predict the relevance solely based on instance-level semantic features and neglect the diverse legal factors (LFs), which are associated with diverse court judgments. Consequently, comprehensively representing a criminal case remains a challenge for these approaches. Moreover, extracting and utilizing these LFs for criminal case matching face two challenges: (1) the manual annotations of LFs rely heavily on specialized legal knowledge; (2) overlaps among LFs may potentially harm the model's performance. In this paper, we propose a two-stage framework named Diverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM). Firstly, DLF-CCM employs a multi-task learning framework to pre-train an LF extraction network on a large-scale legal judgment prediction dataset. In stage two, DLF-CCM introduces an LF de-redundancy module to learn shared LF and exclusive LFs. Moreover, an entropy-weighted fusion strategy is introduced to dynamically fuse the multiple relevance generated by all LFs. Experimental results validate the effectiveness of DLF-CCM and show its significant improvements over competitive baselines. Code: https://github.com/jiezhao6/DLF-CCM.
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- 2024
19. Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
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Xu, Heng, Zhu, Tianqing, Zhang, Lefeng, Zhou, Wanlei, and Zhao, Wei
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Computer Science - Machine Learning ,Computer Science - Cryptography and Security - Abstract
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, have resulted in this need to allow a trained model to forget some of its training data. Existing studies of machine unlearning mainly focus on unlearning requests that forget a cluster of instances or all instances from one class. While these approaches are effective in removing instances, they do not scale to scenarios where partial targets within an instance need to be forgotten. For example, one would like to only unlearn a person from all instances that simultaneously contain the person and other targets. Directly migrating instance-level unlearning to target-level unlearning will reduce the performance of the model after the unlearning process, or fail to erase information completely. To address these concerns, we have proposed a more effective and efficient unlearning scheme that focuses on removing partial targets from the model, which we name "target unlearning". Specifically, we first construct an essential graph data structure to describe the relationships between all important parameters that are selected based on the model explanation method. After that, we simultaneously filter parameters that are also important for the remaining targets and use the pruning-based unlearning method, which is a simple but effective solution to remove information about the target that needs to be forgotten. Experiments with different training models on various datasets demonstrate the effectiveness of the proposed approach.
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- 2024
20. Don't Forget Too Much: Towards Machine Unlearning on Feature Level
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Xu, Heng, Zhu, Tianqing, Zhou, Wanlei, and Zhao, Wei
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Computer Science - Cryptography and Security - Abstract
Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a cluster of instances or all instances belonging to a specific class. These types of unlearning might have a significant impact on the model utility; and they may be inadequate for situations where we only need to unlearn features within instances, rather than the whole instances. Due to the different granularity, current unlearning methods can hardly achieve feature-level unlearning. To address the challenges of utility and granularity, we propose a refined granularity unlearning scheme referred to as ``feature unlearning". We first explore two distinct scenarios based on whether the annotation information about the features is given: feature unlearning with known annotations and feature unlearning without annotations. Regarding unlearning with known annotations, we propose an adversarial learning approach to automatically remove effects about features. For unlearning without annotations, we initially enable the output of one model's layer to identify different pattern features using model interpretability techniques. We proceed to filter features from instances based on these outputs with identifying ability. So that we can remove the feature impact based on filtered instances and the fine-tuning process. The effectiveness of our proposed approach is demonstrated through experiments involving diverse models on various datasets in different scenarios.
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- 2024
21. Rectified Iterative Disparity for Stereo Matching
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Xiao, Weiqing and Zhao, Wei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Both uncertainty-assisted and iteration-based methods have achieved great success in stereo matching. However, existing uncertainty estimation methods take a single image and the corresponding disparity as input, which imposes higher demands on the estimation network. In this paper, we propose Cost volume-based disparity Uncertainty Estimation (UEC). Based on the rich similarity information in the cost volume coming from the image pairs, the proposed UEC can achieve competitive performance with low computational cost. Secondly, we propose two methods of uncertainty-assisted disparity estimation, Uncertainty-based Disparity Rectification (UDR) and Uncertainty-based Disparity update Conditioning (UDC). These two methods optimise the disparity update process of the iterative-based approach without adding extra parameters. In addition, we propose Disparity Rectification loss that significantly improves the accuracy of small amount of disparity updates. We present a high-performance stereo architecture, DR Stereo, which is a combination of the proposed methods. Experimental results from SceneFlow, KITTI, Middlebury 2014, and ETH3D show that DR-Stereo achieves very competitive disparity estimation performance.
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- 2024
22. A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis
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Sidky, Emil Y., Wu, Xiangyi, Duan, Xiaoyu, Huang, Hailing, Zhao, Wei, Zhang, Leo Y., Phillips, John Paul, Zhang, Zheng, Chen, Buxin, Xia, Dan, Reiser, Ingrid S., and Pan, Xiaochuan
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Physics - Medical Physics - Abstract
An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction. Physical DBT data is acquired with a Siemens Mammomat scanner of a structured breast phantom with ICA inserts. Results are shown for both directional TV minimization and filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the ICA objects., Comment: Proceedings paper for accepted contribution to the 8th International Conference on Image Formation in X-Ray Computed Tomography (https://www.ct-meeting.org)
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- 2024
23. Stepwise Regression and Pre-trained Edge for Robust Stereo Matching
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Xiao, Weiqing and Zhao, Wei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Due to the difficulty in obtaining real samples and ground truth, the generalization performance and the fine-tuned performance are critical for the feasibility of stereo matching methods in real-world applications. However, the presence of substantial disparity distributions and density variations across different datasets presents significant challenges for the generalization and fine-tuning of the model. In this paper, we propose a novel stereo matching method, called SR-Stereo, which mitigates the distributional differences across different datasets by predicting the disparity clips and uses a loss weight related to the regression target scale to improve the accuracy of the disparity clips. Moreover, this stepwise regression architecture can be easily extended to existing iteration-based methods to improve the performance without changing the structure. In addition, to mitigate the edge blurring of the fine-tuned model on sparse ground truth, we propose Domain Adaptation Based on Pre-trained Edges (DAPE). Specifically, we use the predicted disparity and RGB image to estimate the edge map of the target domain image. The edge map is filtered to generate edge map background pseudo-labels, which together with the sparse ground truth disparity on the target domain are used as a supervision to jointly fine-tune the pre-trained stereo matching model. These proposed methods are extensively evaluated on SceneFlow, KITTI, Middbury 2014 and ETH3D. The SR-Stereo achieves competitive disparity estimation performance and state-of-the-art cross-domain generalisation performance. Meanwhile, the proposed DAPE significantly improves the disparity estimation performance of fine-tuned models, especially in the textureless and detail regions.
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- 2024
24. SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer
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Zhao, Jie, Guan, Ziyu, Xu, Cai, Zhao, Wei, and Jiang, Yue
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Computer Science - Computation and Language - Abstract
Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content. Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications. The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences. In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues. The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation. The multiple JSCW layers further progressively refine content representations. We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity. Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training. We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines. Our code: https://github.com/jiezhao6/SC2.
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- 2024
25. C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
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Lin, Yiqun, Yang, Jiewen, Wang, Hualiang, Ding, Xinpeng, Zhao, Wei, and Li, Xiaomeng
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views to reconstruct CT, also known as sparse-view reconstruction, can reduce ionizing radiation and further benefit interventional radiology. Compared with sparse-view reconstruction for traditional parallel/fan-beam CT, CBCT reconstruction is more challenging due to the increased dimensionality caused by the measurement process based on cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem, although implicit neural representations have been introduced to enable efficient training, only local features are considered and different views are processed equally in previous works, resulting in spatial inconsistency and poor performance on complicated anatomies. To this end, we propose C^2RV by leveraging explicit multi-scale volumetric representations to enable cross-regional learning in the 3D space. Additionally, the scale-view cross-attention module is introduced to adaptively aggregate multi-scale and multi-view features. Extensive experiments demonstrate that our C^2RV achieves consistent and significant improvement over previous state-of-the-art methods on datasets with diverse anatomy., Comment: Accepted to CVPR 2024
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- 2024
26. Quantum Computing in Wireless Communications and Networking: A Tutorial-cum-Survey
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Zhao, Wei, Weng, Tangjie, Ruan, Yue, Liu, Zhi, Wu, Xuangou, Zheng, Xiao, and Kato, Nei
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Computer Science - Networking and Internet Architecture - Abstract
Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing,achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as Reinforcement Learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we will provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we will introduce the applications of QC in communication and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally,we will discuss remaining open issues before concluding.
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- 2024
27. Presence or Absence: Are Unknown Word Usages in Dictionaries?
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Ma, Xianghe, Schlechtweg, Dominik, and Zhao, Wei
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Computer Science - Computation and Language - Abstract
There has been a surge of interest in computational modeling of semantic change. The foci of previous works are on detecting and interpreting word senses gained over time; however, it remains unclear whether the gained senses are covered by dictionaries. In this work, we aim to fill this research gap by comparing detected word senses with dictionary sense inventories in order to bridge between the communities of lexical semantic change detection and lexicography. We evaluate our system in the AXOLOTL-24 shared task for Finnish, Russian and German languages \cite{fedorova-etal-2024-axolotl}. Our system is fully unsupervised. It leverages a graph-based clustering approach to predict mappings between unknown word usages and dictionary entries for Subtask 1, and generates dictionary-like definitions for those novel word usages through the state-of-the-art Large Language Models such as GPT-4 and LLaMA-3 for Subtask 2. In Subtask 1, our system outperforms the baseline system by a large margin, and it offers interpretability for the mapping results by distinguishing between matched and unmatched (novel) word usages through our graph-based clustering approach. Our system ranks first in Finnish and German, and ranks second in Russian on the Subtask 2 test-phase leaderboard. These results show the potential of our system in managing dictionary entries, particularly for updating dictionaries to include novel sense entries. Our code and data are made publicly available\footnote{\url{https://github.com/xiaohemaikoo/axolotl24-ABDN-NLP}}., Comment: LChange24 Camera Ready
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- 2024
28. Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing
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Zhao, Wei, Li, Zhe, Li, Yige, Zhang, Ye, and Sun, Jun
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Computer Science - Artificial Intelligence - Abstract
Large language models (LLMs) are increasingly being adopted in a wide range of real-world applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs response to harmful prompts and propose a novel defense method termed \textbf{L}ayer-specific \textbf{Ed}iting (LED) to enhance the resilience of LLMs against jailbreak attacks. Through LED, we reveal that several critical \textit{safety layers} exist among the early layers of LLMs. We then show that realigning these safety layers (and some selected additional layers) with the decoded safe response from selected target layers can significantly improve the alignment of LLMs against jailbreak attacks. Extensive experiments across various LLMs (e.g., Llama2, Mistral) show the effectiveness of LED, which effectively defends against jailbreak attacks while maintaining performance on benign prompts. Our code is available at \url{https://github.com/ledllm/ledllm}.
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- 2024
29. AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
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Lu, Weigang, Guan, Ziyu, Zhao, Wei, and Yang, Yaming
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Graph Neural Networks (GNNs) have revolutionized graph-based machine learning, but their heavy computational demands pose challenges for latency-sensitive edge devices in practical industrial applications. In response, a new wave of methods, collectively known as GNN-to-MLP Knowledge Distillation, has emerged. They aim to transfer GNN-learned knowledge to a more efficient MLP student, which offers faster, resource-efficient inference while maintaining competitive performance compared to GNNs. However, these methods face significant challenges in situations with insufficient training data and incomplete test data, limiting their applicability in real-world applications. To address these challenges, we propose AdaGMLP, an AdaBoosting GNN-to-MLP Knowledge Distillation framework. It leverages an ensemble of diverse MLP students trained on different subsets of labeled nodes, addressing the issue of insufficient training data. Additionally, it incorporates a Node Alignment technique for robust predictions on test data with missing or incomplete features. Our experiments on seven benchmark datasets with different settings demonstrate that AdaGMLP outperforms existing G2M methods, making it suitable for a wide range of latency-sensitive real-world applications. We have submitted our code to the GitHub repository (https://github.com/WeigangLu/AdaGMLP-KDD24)., Comment: Accepted by KDD 2024
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- 2024
30. Generalized $\beta$ and $(q,t)$-deformed partition functions with $W$-representations and Nekrasov partition functions
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Liu, Fan, Wang, Rui, Yang, Jie, and Zhao, Wei-Zhong
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High Energy Physics - Theory ,Mathematical Physics - Abstract
We construct the generalized $\beta$ and $(q,t)$-deformed partition functions through $W$ representations, where the expansions are respectively with respect to the generalized Jack and Macdonald polynomials labeled by $N$-tuple of Young diagrams. We find that there are the profound interrelations between our deformed partition functions and the $4d$ and $5d$ Nekrasov partition functions. Since the corresponding Nekrasov partition functions can be given by vertex operators, the remarkable connection between our $\beta$ and $(q,t)$-deformed $W$-operators and vertex operators is revealed in this paper. In addition, we investigate the higher Hamiltonians for the generalized Jack and Macdonald polynomials., Comment: 29 pages. Revised version accepted for publication in Eur. Phys. J. C
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- 2024
31. RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing
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Xiong, Jiamei, Yan, Xuefeng, Wang, Yongzhen, Zhao, Wei, Zhang, Xiao-Ping, and Wei, Mingqiang
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Haze severely degrades the visual quality of remote sensing images and hampers the performance of automotive navigation, intelligent monitoring, and urban management. The emerging denoising diffusion probabilistic model (DDPM) exhibits the significant potential for dense haze removal with its strong generation ability. Since remote sensing images contain extensive small-scale texture structures, it is important to effectively restore image details from hazy images. However, current wisdom of DDPM fails to preserve image details and color fidelity well, limiting its dehazing capacity for remote sensing images. In this paper, we propose a novel unified Fourier-aware diffusion model for remote sensing image dehazing, termed RSHazeDiff. From a new perspective, RSHazeDiff explores the conditional DDPM to improve image quality in dense hazy scenarios, and it makes three key contributions. First, RSHazeDiff refines the training phase of diffusion process by performing noise estimation and reconstruction constraints in a coarse-to-fine fashion. Thus, it remedies the unpleasing results caused by the simple noise estimation constraint in DDPM. Second, by taking the frequency information as important prior knowledge during iterative sampling steps, RSHazeDiff can preserve more texture details and color fidelity in dehazed images. Third, we design a global compensated learning module to utilize the Fourier transform to capture the global dependency features of input images, which can effectively mitigate the effects of boundary artifacts when processing fixed-size patches. Experiments on both synthetic and real-world benchmarks validate the favorable performance of RSHazeDiff over multiple state-of-the-art methods. Source code will be released at https://github.com/jm-xiong/RSHazeDiff.
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- 2024
32. Local-peak scale-invariant feature transform for fast and random image stitching
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Li, Hao, Wang, Lipo, Zhao, Tianyun, and Zhao, Wei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and thus computational pricy, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders, compared with the original SIFT method. Nine large images (over 2600*1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation.
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- 2024
33. A Determination of the Local Gravitational Acceleration for the Tsinghua Tabletop Kibble Balance
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Liu, Weibo, Li, Nanjia, Ma, Yongchao, Hu, Ruo, Wu, Shuqing, Zhao, Wei, Huang, Songling, and Li, Shisong
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Physics - Instrumentation and Detectors - Abstract
The Kibble balance requires a measurement of the local gravitational acceleration, $g$, with a typical relative measurement uncertainty of $10^{-9}$. In this paper, the determination of $g$ for the Tsinghua tabletop Kibble balance is presented. A polynomial fitting method is proposed for blind transfers of the absolute gravitational acceleration using relative gravimeters, showing agreement with the value obtained by the tide correction within a few parts in $10^{9}$. Horizontal and vertical gravity gradients are extracted by mapping the gravity distribution at different heights. The self-attraction effect of major components in the experiment, as well as some time-varying systematic effects, are modeled. The final determination of the gravitational acceleration at the mass position, with an uncertainty of 5.4 $\mu$Gal ($k=2$), is achieved for the Tsinghua tabletop Kibble balance experiment., Comment: 11 figures, submitted to IEEE Trans. Instrum. Meas
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- 2024
34. TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
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Yan, Meng, Huang, Haibin, Liu, Ying, Zhao, Juan, Gao, Xiyue, Xu, Cai, Guan, Ziyu, and Zhao, Wei
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Computer Science - Information Retrieval - Abstract
Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as reviews, images, etc. This content often contains inevitable noise. Some studies attempt to reduce noise interference by suppressing cross-modal inconsistent information. However, they could potentially constrain the capturing of personalized user preferences. In addition, it is almost impossible to entirely eliminate noise in diverse user-generated multi-modal content. To solve these problems, we propose a trustworthy sequential recommendation method via noisy user-generated multi-modal content. Specifically, we explicitly capture the consistency and complementarity of user-generated multi-modal content to mitigate noise interference. We also achieve the modeling of the user's multi-modal sequential preferences. In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective to dynamically evaluate the uncertainty of prediction results. Experimental evaluation on four widely-used datasets demonstrates the superior performance of our model compared to state-of-the-art methods. The code is released at https://github.com/FairyMeng/TrustSR.
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- 2024
35. Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
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Wu, Yang, Wan, Yao, Zhang, Hongyu, Sui, Yulei, Wei, Wucai, Zhao, Wei, Xu, Guandong, and Jin, Hai
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Computer Science - Databases ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.
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- 2024
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36. External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection
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Liang, Wen, Ran, Peipei, Bai, Mengchao, Liu, Xiao, Githinji, P. Bilha, Zhao, Wei, and Qin, Peiwu
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness the potential of transformers for SOD, we propose a novel parameter-efficient fine-tuning method aimed at reducing the number of training parameters while enhancing the salient object detection capability. Our model, termed EXternal Prompt features Enhanced adapteR Tuning (ExPert), features an encoder-decoder structure with adapters and injectors interspersed between the layers of a frozen transformer encoder. The adapter modules adapt the pretrained backbone to SOD while the injector modules incorporate external prompt features to enhance the awareness of salient objects. Comprehensive experiments demonstrate the superiority of our method. Surpassing former state-of-the-art (SOTA) models across five SOD datasets, ExPert achieves 0.215 mean absolute error (MAE) in the ECSSD dataset with 80.2M trained parameters, 21% better than SelfReformer and 47% better than EGNet., Comment: ICPR24 accepted
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- 2024
37. Trusted Multi-view Learning with Label Noise
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Xu, Cai, Zhang, Yilin, Guan, Ziyu, and Zhao, Wei
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Computer Science - Machine Learning ,I.2.6 - Abstract
Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critical applications. To address this issue, researchers propose trusted multi-view methods that learn the class distribution for each instance, enabling the estimation of classification probabilities and uncertainty. However, these methods heavily rely on high-quality ground-truth labels. This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels? We propose a trusted multi-view noise refining method to solve this problem. We first construct view-opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. Subsequently, we design view-specific noise correlation matrices that transform the original opinions into noisy opinions aligned with the noisy labels. Considering label noises originating from low-quality data features and easily-confused classes, we ensure that the diagonal elements of these matrices are inversely proportional to the uncertainty, while incorporating class relations into the off-diagonal elements. Finally, we aggregate the noisy opinions and employ a generalized maximum likelihood loss on the aggregated opinion for model training, guided by the noisy labels. We empirically compare TMNR with state-of-the-art trusted multi-view learning and label noise learning baselines on 5 publicly available datasets. Experiment results show that TMNR outperforms baseline methods on accuracy, reliability and robustness. The code and appendix are released at https://github.com/YilinZhang107/TMNR., Comment: 12 pages, accepted at IJCAI 2024
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- 2024
38. Design of Artificial Interference Signals for Covert Communication Aided by Multiple Friendly Nodes
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Guo, Xuyang Zhao. Wei and Wang, Yongchao
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Electrical Engineering and Systems Science - Signal Processing - Abstract
In this paper, we consider a scenario of covert communication aided by multiple friendly interference nodes. The objective is to conceal the legitimate communication link under the surveillance of a warden. The main content is as follows: first, we propose a novel strategy for generating artificial noise signals in the considered covert scenario. Then, we leverage the statistical information of channel coefficients to optimize the basis matrix of the artificial noise signals space in the absence of accurate channel fading information between the friendly interference nodes and the legitimate receiver. The optimization problem aims to design artificial noise signals within the space to facilitate covert communication while minimizing the impact on the performance of legitimate communication. Second, a customized Rimannian Stochastic Variance Reduced Gradient (R-SVRG) algorithm is proposed to solve the non-convex problem. In the algorithm, we employ the Riemannian optimization framework to analyze the geometric structure of the basis matrix constraints and transform the original non-convex optimization problem into an unconstrained problem on the complex Stiefel manifold for solution. Third, we theoretically prove the convergence of the proposed algorithm to a stationary point. In the end, we evaluate the performance of the proposed strategy for generating artificial noise signals through numerical simulations. The results demonstrate that our approach significantly outperforms the Gaussian artificial noise strategy without optimization.
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- 2024
39. Learning from Shenzhen: China's Post-Mao Experiment from Special Zone to Model City ed. by Mary Ann O'Donnell, Winnie Wong and Jonathan Bach (review)
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Zhao, Wei (Windy)
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- 2019
40. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations
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Lundin, Jessica I, Peters, Ulrike, Hu, Yao, Ammous, Farah, Avery, Christy L, Benjamin, Emelia J, Bis, Joshua C, Brody, Jennifer A, Carlson, Chris, Cushman, Mary, Gignoux, Chris, Guo, Xiuqing, Haessler, Jeff, Haiman, Chris, Joehanes, Roby, Kasela, Silva, Kenny, Eimear, Lapalainien, Tuuli, Levy, Daniel, Liu, Chunyu, Liu, Yongmei, Loos, Ruth JF, Lu, Ake, Matise, Tara, North, Kari E, Park, Sungshim L, Ratliff, Scott M, Reiner, Alex, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Sotoodehnia, Nona, Tracy, Russell, Van den Berg, David, Xu, Huichun, Ye, Ting, Zhao, Wei, Raffield, Laura M, Kooperberg, Charles, and Study, On Behalf of the PAGE
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Biological Sciences ,Genetics ,Minority Health ,American Indian or Alaska Native ,Human Genome ,Health Disparities ,Aetiology ,2.1 Biological and endogenous factors ,Inflammatory and immune system ,Humans ,DNA Methylation ,C-Reactive Protein ,Epigenesis ,Genetic ,DNA ,Inflammation ,Genome-Wide Association Study ,CpG Islands ,Intracellular Signaling Peptides and Proteins ,C-reactive protein ,methylation ,epigenetics ,EWAS ,racial and ethnic diversity ,Mendelian randomization ,causal pathway ,PAGE Study ,Biochemistry and Cell Biology ,Medical Biochemistry and Metabolomics ,Developmental Biology ,Biochemistry and cell biology - Abstract
Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p
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- 2024
41. Characteristics of Online Health Care Services From China’s Largest Online Medical Platform: Cross-sectional Survey Study
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Jiang, Xuehan, Xie, Hong, Tang, Rui, Du, Yanmei, Li, Tao, Gao, Jinsheng, Xu, Xiuping, Jiang, Siqi, Zhao, Tingting, Zhao, Wei, Sun, Xingzhi, Hu, Gang, Wu, Dejun, and Xie, Guotong
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Public aspects of medicine ,RA1-1270 - Abstract
BackgroundInternet hospitals in China are in great demand due to limited and unevenly distributed health care resources, lack of family doctors, increased burdens of chronic diseases, and rapid growth of the aged population. The COVID-19 epidemic catalyzed the expansion of online health care services. In recent years, internet hospitals have been rapidly developed. Ping An Good Doctor is the largest, national online medical entry point in China and is a widely used platform providing online health care services. ObjectiveThis study aims to give a comprehensive description of the characteristics of the online consultations and inquisitions in Ping An Good Doctor. The analyses tried to answer the following questions: (1) What are the characteristics of the consultations in Ping An Good Doctor in terms of department and disease profiles? (2) Who uses the online health services most frequently? and (3) How is the user experience of the online consultations of Ping An Good Doctor? MethodsA total of 35.3 million consultations and inquisitions over the course of 1 year were analyzed with respect to the distributions of departments and diseases, user profiles, and consulting behaviors. ResultsThe geographical distribution of the usage of Ping An Good Doctor showed that Shandong (18.4%), Yunnan (15.6%), Shaanxi (7.2%), and Guangdong (5.5%) were the provinces that used it the most; they accounted for 46.6% of the total consultations and inquisitions. In terms of department distribution, we found that gynecology and obstetrics (19.2%), dermatology (17.0%), and pediatrics (14.4%) were the top three departments in Ping An Good Doctor. The disease distribution analysis showed that, except for nondisease-specific consultations, acute upper respiratory infection (AURI) (4.1%), pregnancy (2.8%), and dermatitis (2.4%) were the most frequently consulted diseases. In terms of user profiles, females (60.4%) from 19 to 35 years of age were most likely to seek consultations online, in general. The user behavior analyses showed that the peak times of day for online consultations occurred at 10 AM, 3 PM, and 9 PM. Regarding user experience, 93.0% of users gave full marks following their consultations. For some disease-related health problems, such as AURI, dermatitis, and eczema, the feedback scores were above average. ConclusionsThe prevalence of internet hospitals, such as Ping An Good Doctor, illustrated the great demand for online health care services that can go beyond geographical limitations. Our analyses showed that nondisease-specific issues and moderate health problems were much more frequently consulted about than severe clinical conditions. This indicated that internet hospitals played the role of the family doctor, which helped to relieve the stress placed on offline hospitals and facilitated people’s lives. In addition, good user experiences, especially regarding disease-related inquisitions, suggested that online health services can help solve health problems. With support from the government and acceptance by the public, online health care services could develop at a fast pace and greatly benefit people’s daily lives.
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- 2021
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42. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.
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Hrytsenko, Yana, Shea, Benjamin, Elgart, Michael, Kurniansyah, Nuzulul, Lyons, Genevieve, Morrison, Alanna, Carson, April, Haring, Bernhard, Mitchell, Braxton, Psaty, Bruce, Jaeger, Byron, Gu, C, Kooperberg, Charles, Levy, Daniel, Lloyd-Jones, Donald, Choi, Eunhee, Brody, Jennifer, Smith, Jennifer, Rotter, Jerome, Moll, Matthew, Fornage, Myriam, Simon, Noah, Castaldi, Peter, Casanova, Ramon, Chung, Ren-Hua, Kaplan, Robert, Loos, Ruth, Kardia, Sharon, Rich, Stephen, Redline, Susan, Kelly, Tanika, OConnor, Timothy, Zhao, Wei, Kim, Wonji, Guo, Xiuqing, Ida Chen, Yii-Der, and Sofer, Tamar
- Subjects
Humans ,Machine Learning ,Blood Pressure ,Multifactorial Inheritance ,Phenotype ,Genome-Wide Association Study ,Risk Factors ,Male ,Female ,Genetic Predisposition to Disease ,Models ,Genetic ,Hypertension ,Middle Aged ,Genetic Risk Score - Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble models performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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- 2024
43. Utilizing (Al, Ga)2O3/Ga2O3 superlattices to measure cation vacancy diffusion and vacancy-concentration-dependent diffusion of Al, Sn, and Fe in \b{eta} -Ga2O3
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Rock, Nathan D., Yang, Haobo, Eisner, Brian, Levin, Aviva, Bhattacharyya, Arkka, Krishnamoorthy, Sriram, Ranga, Praneeth, Walker, Michael A, Wang, Larry, Cheng, Ming Kit, Zhao, Wei, and Scarpulla, Michael A.
- Subjects
Condensed Matter - Materials Science - Abstract
Diffusion of native defects such as vacancies and their interactions with impurities are fundamental in semiconductor crystal growth, device processing, and long-term aging of equilibration and transient diffusion of vacancies are rarely investigated. We used aluminum-gallium oxide/gallium oxide superlattices (SLs) to detect and analyze transient diffusion of cation vacancies during annealing in O2 at 1000-1100 C. Using a novel finite difference scheme for the diffusion equation with time- and space-varying diffusion constant, we extract diffusion constants for Al, Fe, and cation vacancies under the given conditions, including the vacancy concentration dependence for Al. indicate that vacancies present in the substrate transiently diffuse through the SLs, interacting with Sn as it also diffuses. In the case of SLs grown on Sn-doped beta-gallium oxide substrates, gradients observed in the extent of Al diffusion indicate that vacancies present in the substrate transiently diffuse through the SLs, interacting with Sn as it also diffuses. In the case of SLs grown on (010) Fe-doped substrates, the Al diffusion is uniform through the SLs, indicating a depth-uniform concentration of vacancies. We find no evidence in either case for the introduction of gallium vacancies from the free surface at rates sufficient to affect Al diffusion down to ppm concentrations, which has important bearing on the validity of typically-made assumptions of vacancy equilibration. Additionally, we show that unintentional impurities in Sn-doped gallium oxide such as Fe, Ni, Mn, Cu, and Li also diffuse towards the surface and accumulate. Many of these likely have fast interstitial diffusion modes capable of destabilizing devices over time, thus highlighting the importance of controlling unintentional impurities in beta-gallium oxide wafers., Comment: 11 pages, 4 figures, references a supplimental which will be submitted seperately
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- 2024
44. Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
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Zhao, Han, Zhang, Min, Zhao, Wei, Ding, Pengxiang, Huang, Siteng, and Wang, Donglin
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, current MLLMs are composed of the well-known Transformer network, which has a less efficient quadratic computation complexity. To improve the efficiency of such basic models, we propose Cobra, a linear computational complexity MLLM. Specifically, Cobra integrates the efficient Mamba language model into the visual modality. Moreover, we explore and study various modal fusion schemes to create an effective multi-modal Mamba. Extensive experiments demonstrate that (1) Cobra achieves extremely competitive performance with current computationally efficient state-of-the-art methods, e.g., LLaVA-Phi, TinyLLaVA, and MobileVLM v2, and has faster speed due to Cobra's linear sequential modeling. (2) Interestingly, the results of closed-set challenging prediction benchmarks show that Cobra performs well in overcoming visual illusions and spatial relationship judgments. (3) Notably, Cobra even achieves comparable performance to LLaVA with about 43% of the number of parameters. We will make all codes of Cobra open-source and hope that the proposed method can facilitate future research on complexity problems in MLLM. Our project page is available at: https://sites.google.com/view/cobravlm., Comment: Update ablation results
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- 2024
45. Algorithmic Collusion and Price Discrimination: The Over-Usage of Data
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Xu, Zhang, Zhang, Mingsheng, and Zhao, Wei
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Economics - General Economics - Abstract
As firms' pricing strategies increasingly rely on algorithms, two concerns have received much attention: algorithmic tacit collusion and price discrimination. This paper investigates the interaction between these two issues through simulations. In each period, a new buyer arrives with independently and identically distributed willingness to pay (WTP), and each firm, observing private signals about WTP, adopts Q-learning algorithms to set prices. We document two novel mechanisms that lead to collusive outcomes. Under asymmetric information, the algorithm with information advantage adopts a Bait-and-Restrained-Exploit strategy, surrendering profits on some signals by setting higher prices, while exploiting limited profits on the remaining signals by setting much lower prices. Under a symmetric information structure, competition on some signals facilitates convergence to supra-competitive prices on the remaining signals. Algorithms tend to collude more on signals with higher expected WTP. Both uncertainty and the lack of correlated signals exacerbate the degree of collusion, thereby reducing both consumer surplus and social welfare. A key implication is that the over-usage of data, both payoff-relevant and non-relevant, by AIs in competitive contexts will reduce the degree of collusion and consequently lead to a decline in industry profits.
- Published
- 2024
46. High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition
- Author
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Han, Youfang, Zhao, Wei, Chen, Xiangjin, and Meng, Xin
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Human-Computer Interaction ,Computer Science - Neural and Evolutionary Computing - Abstract
Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level sEMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.
- Published
- 2024
47. Reliable Conflictive Multi-View Learning
- Author
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Xu, Cai, Si, Jiajun, Guan, Ziyu, Zhao, Wei, Wu, Yue, and Gao, Xiyue
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML., Comment: 9 pages and to be appeared in AAAI2024
- Published
- 2024
48. NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
- Author
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Zhao, Wei, Hou, Zhitao, Wu, Siyuan, Gao, Yan, Dong, Haoyu, Wan, Yao, Zhang, Hongyu, Sui, Yulei, and Zhang, Haidong
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation., Comment: To appear at EACL 2024
- Published
- 2024
49. Syntactic Language Change in English and German: Metrics, Parsers, and Convergences
- Author
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Chen, Yanran, Zhao, Wei, Breitbarth, Anne, Stoeckel, Manuel, Mehler, Alexander, and Eger, Steffen
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency. Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load. The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years. We base our observations on five dependency parsers, including the widely used Stanford CoreNLP as well as 4 newer alternatives. Our analysis of syntactic language change goes beyond linear dependency distance and explores 15 metrics relevant to dependency distance minimization (DDM) and/or based on tree graph properties, such as the tree height and degree variance. Even though we have evidence that recent parsers trained on modern treebanks are not heavily affected by data 'noise' such as spelling changes and OCR errors in our historic data, we find that results of syntactic language change are sensitive to the parsers involved, which is a caution against using a single parser for evaluating syntactic language change as done in previous work. We also show that syntactic language change over the time period investigated is largely similar between English and German for the different metrics explored: only 4% of cases we examine yield opposite conclusions regarding upwards and downtrends of syntactic metrics across German and English. We also show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions. To our best knowledge, ours is the most comprehensive analysis of syntactic language change using modern NLP technology in recent corpora of English and German., Comment: Updated to the current version
- Published
- 2024
50. A Self-Healing Magnetic-Array-Type Current Sensor with Data-Driven Identification of Abnormal Magnetic Measurement Units
- Author
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Liu, Xiaohu, Ma, Kang, Liu, Jian, Zhao, Wei, Peng, Lisha, Huang, Songling, and Li, Shisong
- Subjects
Electrical Engineering and Systems Science - Signal Processing - Abstract
Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly from the nominal value due to environmental factors poses a potential threat to the measurement accuracy of the current sensor. In light of the need to ensure sustained measurement accuracy over the long term, this paper proposes an innovative self-healing approach rooted in cyber-physics correlation. This approach aims to identify MMUs exhibiting abnormal measurement errors, allowing for the exclusive utilization of the remaining unaffected MMUs in the current measurement process. To achieve this, principal component analysis (PCA) is employed to discern the primary component, arising from fluctuations of the measured current, from the residual component, attributed to the drift in measurement error. This analysis is conducted by scrutinizing the measured data obtained from the MMUs. Subsequently, the squared prediction error (SPE) statistic (also called $Q$ statistic) is deployed to individually identify any MMU displaying abnormal behavior. The experimental results demonstrate the successful online identification of abnormal MMUs without the need for a standard magnetic field sensor. By eliminating the contributions from the identified abnormal MMUs, the accuracy of the current measurement is effectively preserved., Comment: 11 pages, 10 figures
- Published
- 2024
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