Pipeline parallelism has emerged as an indispensable technique for training large deep neural networks. While existing asynchronous pipeline systems address the time bubbles inherent in synchronous architectures, they continue to suffer frominefficiencyandsusceptibilitytovolatilehardware environment due to their suboptimal andstaticconfigurations. In this paper, we propose DynPipe, aninterference-awareasynchronous pipeline framework to optimize theend-to-endtraining performance in highlydynamiccomputing environments. By characterizing thenon-overlappedcommunication overheads andconvergencerate conditioned on stage-wise staleness, DynPipe carefully crafts an optimized pipeline partition that harmonizes the hardware speed with statistical convergence. Moreover, DynPipe deploys anon-intrusiverandom forest model that utilizes runtime stage statistics to evaluate the impact of environmental changes, such as task interference and network jitter, on the training efficiency. Following the evaluation guidance, DynPipe adaptivelyadjustspartition plan to restore both intra and inter-stage load balancing, thereby facilitating seamless pipeline reconfiguration in dynamic environments. Extensive experiments show that DynPipe outperforms state-of-the-art systems, accelerating the time-to-accuracy by1.5-3.4×.

In recent years, scene text spotting approaches have evolved into a multi-modal-based framework. Although previous studies have highlighted the crucial importance of the intrinsic synergy between visual and linguistic features, recent advances in multi-modal-based methods typically adopt an implicit fusion strategy with single granularity features, which cannot fully exploit the prior contextual relationships embedded in visual and semantic information. We argue that directly integrating visual and semantic features is sub-optimal because the multi-granularity structure of scene text images is quite different from that of natural images. To address this, we introduce a novel model called the Multi-Granularity Visual Semantic Interactive Fusion Network (MGN-Net), which comprises a Visual Semantic Multi-Granularity Feature Extraction Network (VSMN) and a Multi-Granularity Graph Fusion Learning Network (MGFN). The VSMN adaptively extracts multi-granularity visual and semantic features from the text image, thereby enriching the textual contextual relations. In the MGFN, a cross-modal and cross-hierarchy graph is constructed to align features from different modalities for deep intra-and inter-fusion. This approach also alleviates the inflexibility of the sequential structure when dealing with images of irregularly curved objects. Furthermore, the cross-hierarchy semantic features are designed to facilitate the training of MGN-Net. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art models. The code will be released in MGN-Net.

Abstract Wireless Sensor Network (WSN) has been widely used with its main characteristics of resilience, scalability, and mobility. However, the node battery capacity in WSN is limited. In addition, in scenes with emergencies such as forest ecological monitoring, data packets of wildfire emergencies have higher real‐time requirements than periodic normal data. Therefore, this paper proposes an energy‐efficient MAC protocol based on Receiver initiation and Multi‐Priority Backoff (RMP‐MAC). RMP‐MAC adopts the wake‐up matching mechanism initiated by the receiver. The sender adjusts its schedule according to the wake‐up information of the receiver to match the receiver, which reduces the energy consumption of idle listening. In addition, RMP‐MAC proposes a multi‐priority factor adaptive backoff mechanism based on event priority, node remaining energy level, and the number of data packets waiting in the transmission queue, ensuring the transmission of emergency data in the actual situation of forest ecological monitoring. Simulation experiments show that compared with PB‐MAC, AP‐MAC, and MPQ‐MAC, RMP‐MAC can reduce energy consumption by 20.22%, 12.47%, and 6.62% respectively. In addition, it has improved in the average collisions, network throughput, and average delay of packets.