Pei Tang

Guoqi Li, Xumin Chen, Pei Tang et al.

Target control of directed networks, which aims to control only a target subset instead of the entire set of nodes in large natural and technological networks, is an outstanding challenge faced in various real world applications. We address one fundamental issue regarding this challenge, i.e., for a given target subset, how to allocate a minimum number of control sources which provide input signals to the network nodes. This issue remains open in general networks with loops. We show that the issue is essentially a path cover problem and can be further converted into a maximum network flow problem. A method termed `Maximum Flow based Target Path-cover' (MFTP) with complexity $O(|V|^{1/2}|E|)$ in which $|V|$ and $|E|$ denote the number of network nodes and edges is proposed. It is also rigorously proven to provide the minimum number of control sources on arbitrary directed networks, whether loops exist or not. We anticipate that this work would serve wide applications in target control of real-life networks, as well as counter control of various complex systems which may contribute to enhancing system robustness and resilience.

Huiang He, Shengchu Zhao, Jianwen Huang et al.

Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these limitations, we propose Hitem3D 2.0, a multi-view guided native 3D texture generation framework that enhances texture quality through the integration of 2D multi-view generation priors and native 3D texture representations. Hitem3D 2.0 comprises two key components: a multi-view synthesis framework and a native 3D texture generation model. The multi-view generation is built upon a pre-trained image editing backbone and incorporates plug-and-play modules that explicitly promote geometric alignment, cross-view consistency, and illumination uniformity, thereby enabling the synthesis of high-fidelity multi-view images. Conditioned on the generated views and 3D geometry, the native 3D texture generation model projects multi-view textures onto 3D surfaces while plausibly completing textures in unseen regions. Through the integration of multi-view consistency constraints with native 3D texture modeling, Hitem3D 2.0 significantly improves texture completeness, cross-view coherence, and geometric alignment. Experimental results demonstrate that Hitem3D 2.0 outperforms existing methods in terms of texture detail, fidelity, consistency, coherence, and alignment.

Tong Piao, Pei Tang, Zhipeng Zhang et al.

In recent years, Large Language Models (LLMs) have been widely applied across various domains due to their powerful domain adaptation capabilities. Previous studies have suggested that diverse, multi-modal data can enhance LLMs' domain adaptation performance. However, this hypothesis remains insufficiently validated in the e-commerce sector. To address this gap, we propose a comprehensive e-commerce multi-task framework and design empirical experiments to examine the impact of diverse data and tasks on LLMs from two perspectives: "capability comprehensiveness" and "task comprehensiveness." Specifically, we observe significant improvements in LLM performance by progressively introducing tasks related to new major capability areas and by continuously adding subtasks within different major capability domains. Furthermore, we observe that increasing model capacity amplifies the benefits of diversity, suggesting a synergistic relationship between model capacity and data diversity. Finally, we validate the best-performing model from our empirical experiments in the KDD Cup 2024, achieving a rank 5 in Task 1. This outcome demonstrates the significance of our research for advancing LLMs in the e-commerce domain.

Shuang Wu, Guanrui Wang, Pei Tang et al.

Compact convolutional neural networks gain efficiency mainly through depthwise convolutions, expanded channels and complex topologies, which contrarily aggravate the training process. Besides, 3x3 kernels dominate the spatial representation in these models, whereas even-sized kernels (2x2, 4x4) are rarely adopted. In this work, we quantify the shift problem occurs in even-sized kernel convolutions by an information erosion hypothesis, and eliminate it by proposing symmetric padding on four sides of the feature maps (C2sp, C4sp). Symmetric padding releases the generalization capabilities of even-sized kernels at little computational cost, making them outperform 3x3 kernels in image classification and generation tasks. Moreover, C2sp obtains comparable accuracy to emerging compact models with much less memory and time consumption during training. Symmetric padding coupled with even-sized convolutions can be neatly implemented into existing frameworks, providing effective elements for architecture designs, especially on online and continual learning occasions where training efforts are emphasized.