Accurate muscle-mass assessment is crucial for staging and managing sarcopenia, yet existing methods suffer from modality-specific limitations and weak integration of muscle function indicators. To solve these limitations, we propose a Dual-source Features Graph for Sarcopenia Evaluation (DFGSE) to synergize high- and low-energy whole-body Dual-energy X-ray Absorptiometry (DXA) images, local high-energy DXA images, and blood-borne biochemical markers. Specifically, the feature extraction module employs dual-energy feature extraction to disentangle soft-tissue and skeletal cues from low-energy images, while skeleton-aware detection extracts joint features from high-energy images. It yields global and local DXA embeddings, complemented by blood-test representations. In the relevance exploration module, inter- and intra-modality correlations are computed via bilinear transformations to form adjacency matrices for the global, local, and blood modality representations. These matrices seed the Multi-type Multi-relation Graph Convolutional Network (MMGCN) – the core of the relation learning module – which captures both direct and indirect interactions among modalities through relation-aware message passing. Finally, the graph-fused representations are used by a muscle-mass prediction head trained with cross-entropy loss. Experiments on the public MURA dataset and two independent sarcopenia cohorts demonstrate that DFGSE consistently outperforms machine learning and state-of-the-art graph-based methods, in terms of four evaluation metrics for classification task.

Although softmax loss and its variants have achieved great success in face verification, the performance is still subject to the data imbalance and early saturation problems. In this paper, we define hard samples as minority class samples and early saturation samples, in order to address both issues, we propose a new loss function termed Hard-Samples based Margin (HSM) loss. Inspired by the class-variant margin normalized softmax loss, we add larger margin on minority classes, the proposed real-class margin overcomes the negative influence from the data imbalance via making the optimization more balanced, while by expanding the margin of early saturated samples, the proposed pseudo-class margin keeps the samples away from the saturation region. Comprehensive experiments show that our HSM loss consistently surpasses the state-of-the-art loss functions on four popular face verification benchmarks.

In radio-frequency identification (RFID) applications, RFID tags attached to new, misplaced, or counterfeited commodities sometimes may not be timely registered and are unknown for readers. In applications like inventory management and product tracking, these unknown tags pose several challenges for fast tag identification. A simple method to identify unknown tags is to first deactivate the registered known tags, and then collect IDs of the unknown ones. However, this is a nontrivial task. In fact, unknown tags cause interference with the deactivation of known tags. Moreover, the unknown tag collection methods used in existing protocols either suffer severe tag collisions or generate many empty slots, which increases the final execution time. In this article, we propose an efficient unknown tag identification (EUTI) protocol. First, EUTI builds a vector-based filter to exclude the tags that are not expected to reply in each slot, so that EUTI can use both predicted collision slots and singleton slots for unknown tag deactivation and avoid collisions caused by unknown tags. Second, EUTI adopts a reservation mechanism to reduce collision slots and guide each unknown tag to skip empty slots when replying, thus saving execution time. Moreover, we provide a theoretical analysis of EUTI to minimize execution time and extend EUTI to multi-reader scenarios. Numerical results show that EUTI outperforms the state-of-the-art solutions by reducing up to 44.12% in deactivation time, 26.47% in collection time, and 27.75% in total time.

In recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, plenty of deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace. These methods only model the distribution at the identity-level but ignore the latent relationship between frontal and profile viewpoints. Different from these methods, FCGFace takes viewpoints into consideration by modeling the distribution at both the identity-level and the viewpoint-level. At the identity-level, a softmax-based loss is employed for a relatively rough classification. At the viewpoint-level, centers of frontal face features are defined to guide the optimization conducted in a more refined way. Specifically, our FCGFace is capable of adaptively adjusting the distribution of profile face features and narrowing the gap between them and frontal face features during different training stages to form compact identity clusters. Extensive experimental results on popular benchmarks, including cross-pose datasets (CFP-FP, CPLFW, VGGFace2-FP, and Multi-PIE) and non-cross-pose datasets (YTF, LFW, AgeDB-30, CALFW, IJB-B, IJB-C, and RFW), have demonstrated the superiority of our FCGFace over the SOTA competitors.