Addressing the inefficiency of marital matching in aging societies, this study proposes HBRA, a bidirectional recommendation framework leveraging heterogeneous graph learning to model asymmetric preferences and enhance compatibility analysis. The framework integrates behavioral and textual data through a unified graph architecture, overcoming limitations of unidirectional recommendation systems and fragmented feature fusion. By introducing spectral graph theory and meta-path-guided message passing, HBRA achieves noise-robust preference modeling while eliminating traditional neural network training through closed-form matrix decomposition, reducing computational complexity by 99.5%. Evaluated on the novel “FCWR” dataset (built from real-world matchmaking records) and the Speed Dating benchmark, HBRA outperforms 24 state-of-the-art models, improving NDCG@2 by 25.8% and training efficiency by three orders of magnitude. Parameter analysis reveals text-based lifestyle keywords as the dominant matching factor, surpassing traditional attributes like age and geography. This work provides a scalable, interpretable solution for intelligent matchmaking platforms, directly addressing demographic challenges posed by population aging through data-driven compatibility optimization.

Estimating 3D hand shape from a single-view RGB image is important for many applications. However, the diversity of hand shapes and postures, depth ambiguity, and occlusion may result in pose errors and noisy hand meshes. Making full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape estimation. In this paper, we use 2D joint heatmaps to obtain spatial details for robust pose estimation. We also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image alignment. Our method has four cascaded stages: 2D cue extraction, pose feature encoding, initial reconstruction, and reconstruction refinement. Specifically, we first encode the image to determine semantic features during 2D cue extraction; this is also used to predict hand joints and for segmentation. Then, during the pose feature encoding stage, we use a hand joints encoder to learn spatial information from the joint heatmaps. Next, a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step; a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand structures. Finally, a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh, to predict an offset hand mesh to fine-tune the reconstruction results. Quantitative and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance.

In this paper, we propose a multi-level fault-tolerant storage cluster called MFTS, which provides flexible reliability for a wide variety of applications. MFTS makes use of a reliability upper-bound (i.e., Parameter r) to guide the process of adjusting fault-tolerance levels, i.e., i-erasure(s) and i {1, 2, .. ., r}. In particular, MFTS can map an appropriate coding scheme to an application with individual reliability requirements. MFTS is capable of partitioning multi-level reliable storage using a virtual storage space, thereby adapting to any changing reliability demands of applications. We present the implementation of the MFTS system, which adopts an intersecting zigzag sets code (IZS code) rather than replication or general-purpose erasure codes. Our MFTS has three salient features: partial updates, fast reconstructions, and minimal overhead of fault-tolerance level transitions. To quantify performance optimization in our storage cluster, we compare IZS-enabled MFTS with two storage clusters equipped with the Vandermondeand Cauchy-Reed-Solomon codes. The experimental results show that: 1) three schemes have comparable user-response-time performance in both the operational and degraded modes; 2) MFTS outperforms the other two alternatives by up to 26.1 percent in the offline reconstruction case; 3) MFTS speeds up the online reconstruction by up to 23.7 percent over the other two schemes with marginal increase in user response time.

We present a power-efficient scheme for erasure-coded storage clusters---ECS2---which aims to offer high energy efficiency with marginal reliability degradation. ECS2utilizes data redundancies and deferred writes to conserve energy. In ECS2parity blocks are buffered exclusively in active data nodes whereas parity nodes are placed into low-power mode. (k+r, k) RS-coded ECS2can achieve ⌈(r+ 1)/2⌉-fault tolerance forkactive data nodes andr-fault tolerance for allk+rnodes. ECS2employs the following three optimizing approaches to improve the energy efficiency of storage clusters. (1) An adaptive threshold policy takes system configurations and I/O workloads into account to maximize standby time periods; (2) a selective activation policy minimizes the number of power-transitions in storage nodes; and (3) a region-based buffer policy speeds up the synchronization process by migrating parity blocks in a batch method. After implementing an ECS2-based prototype in a Linux cluster, we evaluated its energy efficiency and performance using four different types of I/O workloads. The experimental results indicate that compared to energy-oblivious erasure-coded storage, ECS2can save the energy used by storage clusters up to 29.8% and 28.0% in read-intensive and write-dominated workloads when k = 6 and r = 3, respectively. The results also show that ECS2accomplishes high power efficiency in both normal and failed cases without noticeably affecting the I/O performance of storage clusters.

Nowadays, erasure codes have been widely used in data storage to achieve high fault-tolerance. However, compared with replica-based storage, erasure-coded system may suffer significant performance overhead in encoding, decoding and updating. Traditional updating schemes(e.g. DUM and PUM) use an individual manager node to accomplish the updating. In this paper, we propose two partial-updating schemes (i.e. PUM-P and PDN-P) to improve the small update in erasure coded storage clusters, where both schemes only read a portion of data, including the data blocks to be updated and the parity blocks, and utilize the calculation capacity of the storage nodes. We implement four updating algorithms (DUM, PUM, PUM-P and PDN-P) upon an erasure-coded storage cluster platform, and conduct a set of comparative tests under two real-world workloads with different fault-tolerance parameters. The experimental results shows that PUM-P and PDN-P can speed up the small updating by a factor of up to 1.62 and 2.72 compared with DUM under small update, respectively; and by a factor of up to 1.42 and 2.23 relative to PUM, respectively. We also validate that DUM scheme can achieve better updating performance than the other schemes within the large update scenario.