In this study, the feasibility of assessing the chemical composition in bone using the multi-wavelength photoacoustic analysis (MWPA) method was investigated. By illuminating a bone specimen using laser light with a wavelength tunable over an optical spectrum from 680 nm to 950 nm, the optical absorption spectrum of the bone was acquired. Then, with the optical absorption spectra of all the optically absorbing chemical components in the bone known, a spectral unmixing procedure was performed to quantitatively assess the relative content of each chemical component. The experimental results from porcine rib bones demonstrated that the contents of the chemical components, including not only non-organic materials such as minerals and water but also organic materials including oxygenated hemoglobin, deoxygenated hemoglobin, lipid, and collagen, can all be assessed by MWPA. As the chemical composition in the bone is directly associated with functional and metabolic activities, the finding from this study suggests that the MWPA method could offer a new diagnostic tool for the non-invasive evaluation of bone health.

We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a particular subgroup of nodes in GNN-based node classification, where nodes of the underlying network have sensitive attributes, such as race or gender. We conduct qualitative and experimental analyses explaining how adversarial link injection impairs the fairness of GNN predictions. For example, an attacker can compromise the fairness of GNN-based node classification by injecting adversarial links between nodes belonging to opposite subgroups and opposite class labels. Our experiments on empirical datasets demonstrate that adversarial fairness attacks can significantly degrade the fairness of GNN predictions (attacks are effective) with a low perturbation rate (attacks are efficient) and without a significant drop in accuracy (attacks are deceptive). This work demonstrates the vulnerability of GNN models to adversarial fairness attacks. We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.

Heterogeneous Information Networks (HINs) are prevalent in real-world systems. Recent advances in network embedding provide an effective way of encoding HINs into low-dimensional vectors. However, there is a growing concern that existing HIN embedding algorithms may suffer from the problem of generating biased representations, resulting in discrimination against certain demographic groups. In this paper, we propose a flexible debiasing framework for fair HIN embedding to address this issue. Specifically, we first formalize measurements and the definition of fairness in HIN embedding. Then, we propose a debiasing framework named FairHGNN, including a novel meta-path sampling method that focuses on mitigating the bias in random walks, and a fairness constraint with Wasserstein distance to alleviate the algorithmic bias in Graph Neural Networks (GNNs). Experimental results on real-world datasets validate the efficacy of FairHGNN in promoting fairness and maintaining good utility.