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pro vyhledávání: '"Liu, Yuxi"'
Datasets may include errors, and specifically violations of integrity constraints, for various reasons. Standard techniques for ``minimal-cost'' database repairing resolve these violations by aiming for minimum change in the data, and in the process,
Externí odkaz:
http://arxiv.org/abs/2410.16501
Autor:
Liu, Yuxi
We present a geometrical way of understanding the dynamics of wavefunctions in a free space, using the phase-space formulation of quantum mechanics. By visualizing the Wigner function, the spreading, shearing, the so-called "negative probability flow
Externí odkaz:
http://arxiv.org/abs/2409.02962
Autor:
Liu, Yuxi, Xiao, Mingyu
The \textsc{Co-Path/Cycle Packing} problem (resp. The \textsc{Co-Path Packing} problem) asks whether we can delete at most $k$ vertices from the input graph such that the remaining graph is a collection of induced paths and cycles (resp. induced path
Externí odkaz:
http://arxiv.org/abs/2406.10829
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities an
Externí odkaz:
http://arxiv.org/abs/2406.00758
Sequential recommendation effectively addresses information overload by modeling users' temporal and sequential interaction patterns. To overcome the limitations of supervision signals, recent approaches have adopted self-supervised learning techniqu
Externí odkaz:
http://arxiv.org/abs/2405.20878
Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted for medical image segmentation. However, medical imaging data typical
Externí odkaz:
http://arxiv.org/abs/2403.05408
Learned Image Compression (LIC) has shown remarkable progress in recent years. Existing works commonly employ CNN-based or self-attention-based modules as transform methods for compression. However, there is no prior research on neural transform that
Externí odkaz:
http://arxiv.org/abs/2403.00628