Surgical workflow recognition (SWR) stands as a pivotal component in computer-assisted surgery and is dedicated to identifying phases from surgical videos. Many deep learning-based methods have been proposed for this task and achieved acceptable SWR results. However, these methods usually implicitly extract and aggregate spatio-temporal features, so that it is challenging for these methods to adequately use some spatial information that is strongly relevant to surgical phase in SWR task, such as the information from the surgical instruments. To address this issue, an Explicit Surgical Instrument Prompting (ESIP) approach is proposed for SWR task. ESIP leverages surgical instrument segmentation to generate instrument-specific visual prompts, which explicitly guide the extraction of crucial intra-frame spatial features through a frozen pre-trained backbone, then enable effective inter-frame spatio-temporal feature extraction and aggregation. Unlike multi-task approaches that jointly perform SWR with auxiliary tasks within a shared network framework, ESIP is a single-task SWR approach dedicated to optimize framework itself for more adequate feature extraction. Furthermore, to accomplish the segmentation prompting efficiently, this paper presents SAM-based segmentation with prompt tuning strategy to explicitly integrate segmentation features into spatial features. Experimental results on Cholec80, M2CAI and AutoLaparo datasets demonstrate that our ESIP method achieves the best performance in comparison with 16 SOTA methods, with a Precision of 91.8%, 89.5% and 89.6%, Recall of 92.2%, 89.5% and 76.9%, Jaccard of 83.3%, 77.0% and 67.3%, respectively.

Accurate detection of surgical instruments is critical for both routine surgical procedures and surgical robotics research. To the best of our knowledge, there is a notable lack of datasets and dedicated detection studies specifically addressing orthopedic surgical instruments. Detecting orthopedic surgical instruments presents particular challenges including significant size variations, highly similar shapes, and frequent, severe occlusions due to instrument intersections. To address these issues, we propose an orthopedic surgical instrument detection method (OrthoDetNet) incorporating three specialized modules. The FilterUnit mitigates occlusion effects via an adaptive feature filtering mechanism, that dynamically adjusts its filtering strategy based on context, prioritizing features from key regions while suppressing distracting interference features. The DEUnit enhances fine-grained feature discrimination in local regions to distinguish instruments with high shape similarity, and the BDFusion module improves multi-scale detection performance through bi-directional feature fusion between deep and shallow-level feature maps. A dataset for orthopedic surgical instrument detection is created, which is based on the proximal femoral nail antirotation (PFNA) instrument package manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. Images were captured in a controlled, simulated experimental environment, ensuring no patient privacy or ethical concerns. We obtained explicit authorization from the manufacturer for instrument use. Experimental results on this dataset demonstrate the effectiveness of the OrthoDetNet and its constituent modules.

Temporal graphs attach time information to edges and are commonly used for implementing time-critical applications that can not be effectively processed by traditional static and dynamic graph processing engines. State-of-the-art solutions that target temporal path problems remain ad-hoc and often suboptimal. A unified and high-performance solution that could efficiently process general temporal path problems via a universal optimization strategy and relieve practitioners from heavy optimization efforts is in urgent demand. In this paper, we make two key observations: (1) temporal path problems can be described as topological-optimum problems and solved by a universal single scan execution model; and (2) data redundancy commonly occurs in the native format of the transformed temporal graphs, which is unnecessary for information propagation and can be eliminated for better memory utilization and execution efficiency. Based on these core insights, we propose TegRaph, the first general-purpose temporal graph computing engine to provide a unified optimization strategy and execution model for general temporal path problems and their applications. TegRaph not only presents temporal information-aware graph representation that naturally fits temporal graphs but also offers general system-level supports such as out-of-core execution. Extensive evaluation reveals that TegRaph can achieve significant speedups over the state-of-the-art designs with up to two orders of magnitude (241×) with the throughput of two hundred million edges per second.

Emerging on-volatile memory (NVM) opens an opportunity to build durable data structures. However, to build a highly efficient complex data structure like B+tree on NVM is not easy. We investigate the essential performance bottleneck for NVM-based B+tree. Even with a single-core CPU, the performance is limited by the atomic-write size which plays an essential role in the trade-off between the persistent overhead and keeping leaf node entries sorted. For the multi-core setting, the overlapping of concurrency and persistency is key to the system scalability.

Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability, and fast access at the same time. It is suggested that programs should access NVM directly through CPU load and store instructions. To guarantee crash consistency, durable transactions are regarded as a common choice of applications for accessing persistent memory data. However, existing durable transaction systems employ either undo logging , which requires a fence for every memory write, or redo logging , which requires intercepting all memory reads within transactions. Both approaches incur significant overhead. This article presents D ude T x , a crash-consistent durable transaction system that avoids the drawbacks of both undo and redo logging. D ude T x uses shadow DRAM to decouple the execution of a durable transaction into three fully asynchronous steps. The advantage is that only minimal fences and no memory read instrumentation are required. This design enables an out-of-the-box concurrency control mechanism, transactional memory or fine-grained locks, to be used as an independent component. The evaluation results show that D ude T x adds durability to a software transactional memory system with only 7.4%--24.6% throughput degradation. Compared to typical existing durable transaction systems, D ude T x provides 1.7× --4.4× higher throughput. Moreover, D ude T x can be implemented with hardware transactional memory or lock-based concurrency control, leading to a further 1.7× and 3.3× speedup, respectively.

Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, it has been shown by empirical evidence that programs should access NVM directly through CPU load and store instructions, so that the overhead of a traditional file system or database can be avoided. Thus, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. However, existing durable transaction systems employ either undo logging, which requires a fence for every memory write, or redo logging, which requires intercepting all memory reads within transactions.