Erasure coding plays a crucial role in distributed storage systems to provide fault tolerance at a low storage cost. Conventional erasure coding schemes determine data placement based on stripes. However, placing data into stripes can incur non-negligible performance overheads that will manifest in emerging fast in-memory storage systems, making conventional erasure coding schemes suboptimal in such scenarios. Aiming to eliminate such overheads, we present Nos , a stripeless placement scheme for erasure-coded distributed in-memory storage. It lets each node independently replicate data to other nodes and encode received data replicas into parities with XOR. Thus, it avoids the overheads caused by stripes. To enable failure recovery, Nos uses a combinatoric structure called symmetric balanced incomplete block design (SBIBD) to decide primary-to-backup node affinities during replication. Atop Nos , we further build Nostor , a distributed in-memory key-value store. We also achieve high availability and efficient foreground serving simultaneously with Hotness-aware Two-phase Reconstruction ( HR ). Evaluations demonstrate that Nostor achieves 1.61× to 2.60× throughputs compared to Cocytus , PQ , and Split with similar or lower latencies than these stripe-based erasure coding baselines. Equipped with HR , Nostor + HR also achieves 46.7% P999 foreground latency reduction during node repair.

Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and adaptive grasp transitions remains a challenge, particularly when dealing with external forces and complex motion constraints. Existing grasp transition strategies often fail to account for varying external forces and do not optimize motion performance effectively. In this work, we propose an Imitation-Guided Bimanual Planning Framework that integrates efficient grasp transition strategies and motion performance optimization to enhance stability and dexterity in robotic manipulation. Our approach introduces Strategies for Sampling Stable Intersections in Grasp Manifolds for seamless transitions between uni-manual and bi-manual grasps, reducing computational costs and regrasping inefficiencies. Additionally, a Hierarchical Dual-Stage Motion Architecture combines an Imitation Learning-based Global Path Generator with a Quadratic Programming-driven Local Planner to ensure real-time motion feasibility, obstacle avoidance, and superior manipulability. The proposed method is evaluated through a series of force-intensive tasks, demonstrating significant improvements in grasp transition efficiency and motion performance. A video demonstrating our simulation results can be viewed at https://youtu.be/3DhbUsv4eDo.

With the emergence of fast NVMe SSDs, key-value stores are becoming more CPU-efficient in order to reap their bandwidth. However, current CPU-optimized key-value stores adopt suboptimal intra- and inter-thread models, hence incurring memory-level stalling and load imbalance that hinder cores from realizing their full potential.We present STAGEWISE, an CPU-efficient key-value store on fast NVMe SSDs with high throughput. To achieve this, we introduce a new thread model for StageWise to process KV requests. Specifically, STAGEWISEconverts the processing of each KV request into multiple asynchronous stages, and thus enables pipelining across all stages. STAGEWISEfurther introduces a client-driven share-index architecture to ease inter-thread load imbalance and maximize the pipelining opportunity. Guided by Little’s Law, STAGEWISEimproves concurrency, and therefore efficiently uses CPU to reach higher throughput. Extensive experimental results show that STAGEWISEoutperforms CPUoptimized key-value stores (e.g., KVell) by up to 3.5× with writeintensive workloads, and storage-optimized ones (e.g., RocksDB) by over an order of magnitude. STAGEWISEalso shows higher read performance and excellent scalability under various workloads.

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage group-based training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art.