The rapid evolution of cloud computing, big data, and Artificial Intelligence (AI) technologies has created an urgent demand for ultra-high bandwidth and ultra-low latency in modern data centers. Although Remote Direct Memory Access (RDMA) technology has been widely adopted, traditional congestion control algorithms such as Priority-based Flow Control (PFC) and Data Center Quantized Congestion Notification (DCQCN) face significant challenges in scalability, deployment flexibility, and tail latency management in complex network environments. To address these limitations, we propose SFCC, a sender-driven rate control solution based on Round-Trip Time (RTT) that requires no switch configuration. SFCC incorporates an adaptive Additive Increase Multiplicative Decrease (AIMD) control loop for enhanced scalability and latency reduction, an RTT re-estimation mechanism to correct deviations, dynamic RTT calculation for optimized queue management, and Negative Acknowledgment (NAK) signal utilization for fast convergence during packet loss. Implemented on commercial RDMA Network Interface Cards (NICs) using a Programmable Congestion Control (PCC) platform, SFCC has been evaluated through small-scale testbeds, storage network testbeds, and NS-3 simulations of ultra-large-scale incast scenarios. Experimental results demonstrate that SFCC significantly improves throughput while reducing switch queue lengths, decreases the 99th percentile tail latency by 55.2 %, maintains high reliability under packet loss conditions, and achieves up to$\mathbf{7 5. 4 \%}$reduction in flow completion time for small flows compared to DCQCN, along with faster convergence speed.

Large Language Model (LLM) training increasingly relies on Remote Direct Memory Access (RDMA) to enable ultra-efficient networking. However, the unique traffic characteristics—sparse yet bandwidth-intensive—often lead to severe load imbalance under Equal-Cost Multi-Path (ECMP) routing. Packet Spraying (PS) offers a promising solution by distributing traffic across multiple paths, but its impact on congestion dynamics remains insufficiently studied. This paper presents a comprehensive study of PS in Artificial Intelligence (AI) clusters using NS-3 simulations, analyzing its effects on congestion distribution, packet reordering, and flow completion time. Our findings show that congestion patterns vary significantly with workload intensity and oversubscription ratios, and existing congestion control schemes are inadequate for general PS networks, where both routing paths and congestion hotspots frequently change. To address this gap, we propose APSCC, a congestion control algorithm that infers congestion locations from out-of-order packets and aggregates Explicit Congestion Notification (ECN) signals across paths for precise rate adaptation. Compared to state-of-the-art mechanisms, APSCC reduces Job Completion Time (JCT) by up to 30 %. The implementation is publicly available at https://github.com/tangjianback/APSCC.

Mobile health (mHealth) applications, benefiting from mobile computing, have generated numerous mHealth data. However, they are dispersed across isolated devices, which hinders discovering insights underlying the aggregated data. Considering the online characteristics of mHealth, in this work, we present the first online dataVAluation andPricing mechanism, namely VAP, to incentive users to contribute mHealth data for machine learning (ML) tasks in mHealth systems. Under the Bayesian framework, we propose a new metric based on the concept of entropy to calculate data valuation during model training in an online manner. In proportion to the data valuation, we then determine payments as compensations for users to contribute their data. We formulate this pricing problem as a contextual multi-armed bandit with the goal of profit maximization and propose a new algorithm based on the characteristics of pricing. Furthermore, to tackle the budget constraint, we incorporate a two-stage multi-armed bandit with a knapsack method. We also extend VAP to advanced ML models by computing the entropy on the prediction space. Finally, we have evaluated VAP on two real-world mHealth data sets. Evaluation results show that VAP outperforms the state-of-the-art data valuation and pricing mechanisms in terms of computational complexity and extracted profit.

End-to-end retrieval models, such as Tree-based Models (TDM) and Deep Retrieval (DR), have attracted a lot of attention, but they cannot handle cold-start and long-tail item recommendation scenarios well. Specifically, DR learns a compact indexing structure, enabling efficient and accurate retrieval for large recommendation systems. However, it is discovered that DR largely fails on retrieving cold-start and long-tail items. This is because DR only utilizes user-item interaction data, which is rare and often noisy for cold-start and long-tail items. Besides, end-to-end retrieval models are unable to make use of the rich item content features. To address this issue while maintaining the efficiency of DR indexing structure, we propose Full Index Deep Retrieval (FIDR) that learns indices for the full corpus items, including cold-start and long-tail items. In addition to the original structure in DR (called User Structure in FIDR) that learns with user-item interaction data (e.g., clicks), we add an Item Structure to embed items directly based on item content features (e.g., categories). With joint efforts of User Structure and Item Structure, FIDR makes cold-start items retrievable and also improves the recommendation quality of long-tail items. To our best knowledge, FIDR is the first to solve the cold-start and long-tail recommendation problem for the end-to-end retrieval models. Through extensive experiments on three real-world datasets, we demonstrate that FIDR can effectively recommend cold-start as well as long-tail items, and largely promote overall recommendation performance without sacrificing inference efficiency. According to the experiments, the recall of FIDR is improved by 8.8%~11.9%, while the inference of FIDR is as efficient as DR.

Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive awareness of them. In this work, we propose a multi-modal and rich-annotated knowledge repository, OakInk, for visual and cognitive understanding of hand-object interactions. We start to collect 1,800 common household objects and annotate their affordances to construct the first knowledge base: Oak. Given the affordance, we record rich human interactions with 100 selected objects in Oak. Finally, we transfer the interactions on the 100 recorded objects to their virtual counterparts through a novel method: Tink. The recorded and transferred hand-object interactions constitute the second knowledge base: Ink. As a result, OakInk contains 50,000 distinct affordance-aware and intent-oriented hand-object interactions. We benchmark OakInk on pose estimation and grasp generation tasks. Moreover, we propose two practical applications of OakInk: intent-based interaction generation and handover generation. Our dataset and source code are publicly available at www.oakink.net.

Mobile health (mHealth) applications, benefiting from mobile computing, have emerged rapidly in recent years, and generated a large volume of mHealth data. However, these valuable data are dispersed across isolated devices or organizations, which hinders discovering insights underlying the aggregated data. Considering the online characteristics of mHealth tasks, there is an urgent need for online data acquisition. In this paper, we present the first online data Valuation And Pricing mechanism, namely VAP, to incentive users to contribute mHealth data for machine learning (ML) tasks in mHealth systems. Under the framework of Bayesian ML, we propose a new metric based on the concept of entropy, to evaluate data valuation during model training in an online manner. In proportion to the data valuation, we then determine payments as compensations for users to contribute their data. We formulate this pricing problem as a contextual multi-armed bandit with the goal of profit maximization and propose a new algorithm based on the characteristics of pricing. We also extend VAP to general ML models. Finally, we have evaluated VAP on two real-world mHealth data sets. Evaluation results show that VAP outperforms the state-of-the-art valuation and pricing mechanisms in terms of computational complexity and extracted profit.