Diffusion models have achieved remarkable progress in text-to-image generation, enabling the rise of personalized models. A key challenge in personalized generation is to provide users with precise control while ensuring high fidelity to the content. To address this, we introduce VariGen: a framework that empowers users to achieve fine-grained, layout-controllable personalized image generation. VariGen employs the Variational Detail-Aware Feature Extractor to capture intricate details from reference subjects and the Dual Layout Control Mechanism to integrate layout specifications seamlessly into the generation process. We demonstrate that VariGen achieves superior performance through extensive experimentation, offering unparalleled creative freedom and fidelity. To our knowledge, this is the first work to enable users to “create anything, anywhere” with such precision and flexibility.

As modern learning-to-rank (LTR) systems rely on both textual and behavioral features, it is essential to extend pre-trained language models (PLMs) from text (queries and webpages) understanding to end-to-end ranking score prediction subject to multiple objectives, such as relevance, quality, authority, and recency. While textual inputs encompass a broader array of features than mere relevance and behavioral features are frequently skewed by user feedback with position bias, an integrated solution is required to jointly disentangle and fuse these heterogeneous features, ensuring robust and unbiased ranking predictions. In this work, we introduce RankExpert, a unified framework that holistically models heterogeneous ranking signals by integrating PLM-based semantic extraction with behavioral cues. RankExpert employs a lightweight PLM with hierarchical distillation for efficient query-document representation, a mixture-of-experts (MoE) architecture to disentangle and optimize objectives across relevance, quality, authority, and recency, and a click expert to mitigate position bias in user feedback within a dual-tower design. Then, RankExpert utilizes an adaptive weight fusion layer to dynamically integrate the outputs of these specialized experts dynamically, aligning ranking predictions with diverse user intents. Extensive offline evaluations on two large-scale real-world datasets reveal that RankExpert significantly outperforms strong competitor models and substantial gains in key performance indicators. Moreover, we deploy RankExpert at Baidu Search and conduct comprehensive online evaluations with real-world web traffic, demonstrating substantial improvements in user satisfaction metrics over the online legacy system and underscoring the practicality of the proposed approach.

The map matching of cellular data reconstructs real trajectories of users by exploiting the sequential connections between mobile devices and cell towers. The difficulty in obtaining paired cellular-GPS data and the cellular variation compromise the accuracy and reliability of existing map matching approaches. In this paper, we propose a novelunsupervisedconvolutional network for cellularmapmatching (UCMM) to address these challenges. UCMM employs a dual encoder-decoder network to capture a shared representation from both the cellular and GPS domains in an unsupervised manner. It leverages a dedicated convolutional architecture to tackle the varying lengths of output sequential data. An attention mechanism is specially introduced to deal with the cellular variation. The effectiveness of UCMM is demonstrated through comprehensive evaluations, which show that UCMM achieves a substantial improvement in matching accuracy and deduction of training time compared with the best-known prior works. These improvements make UCMM a significant advancement in the field of map matching.

Flow monitoring is widely applied in software-defined networks (SDNs) for monitoring network performance. Especially, detecting heavy hitters can prevent the Distributed Denial of Service (DDoS) attack. However, many existing approaches fall into one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) sacrifice of accuracy due to fast detection. One practical problem with this is that it does not have the flexibility to adjust the monitoring strategy to the monitoring needs, making it difficult to meet different applications. To alleviate this problem, we propose our design of a novel flow monitoring framework that keeps the balance between accuracy and efficiency. It provides customized monitoring services for applications, where network resources can be saved, and the error rate can also be confined. In this paper, we present cReFeR, a three-step “compression Report-Feedback-Report” framework to monitor SDNs. The IP and the value compressor are specially designed to reduce the volume of flow statistics collection. This framework thus can achieve accuracy-ensured and resource-saving flow monitoring in SDNs. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. cReFeR keeps the error rate under 3% and reduces the amount of monitoring data more than 40%, which guarantees high efficiency compared with existing methods.