Digital Holographic Imaging via Direct Quantum Wavefunction Reconstruction

Wavefunction is a fundamental concept of quantum theory. Recent studies have shown surprisingly that wavefunction can be directly reconstructed via the measurement of weak value. The weak value based direct wavefunction reconstruction not only gives the operational meaning of wavefunction, but also provides the possibility of realizing holographic imaging with a totally new quantum approach. Here, we review the basic background knowledge of weak value based direct wavefunction reconstruction combined with recent experimental demonstrations. The main purpose of this work focuses on the idea of holographic imaging via direct wavefunction reconstruction. Since research on this topic is still in its early stage, we hope that this work can attract interest in the field of traditional holographic imaging. In addition, the wavefunction holographic imaging may find important applications in quantum information science.Comment: comments are welcom

VIGraph: Self-supervised Learning for Class-Imbalanced Node Classification

Class imbalance in graph data poses significant challenges for node classification. Existing methods, represented by SMOTE-based approaches, partially alleviate this issue but still exhibit limitations during imbalanced scenario construction. Self-supervised learning (SSL) offers a promising solution by synthesizing minority nodes from the data itself, yet its potential remains unexplored. In this paper, we analyze the limitations of SMOTE-based approaches and introduce VIGraph, a novel SSL model based on the self-supervised Variational Graph Auto-Encoder (VGAE) that leverages Variational Inference (VI) to generate minority nodes. Specifically, VIGraph strictly adheres to the concept of imbalance when constructing imbalanced graphs and utilizes the generative VGAE to generate minority nodes. Moreover, VIGraph introduces a novel Siamese contrastive strategy at the decoding phase to improve the overall quality of generated nodes. VIGraph can generate high-quality nodes without reintegrating them into the original graph, eliminating the "Generating, Reintegrating, and Retraining" process found in SMOTE-based methods. Experiments on multiple real-world datasets demonstrate that VIGraph achieves promising results for class-imbalanced node classification tasks

Association between TGFBR1*6A and osteosarcoma: A Chinese case-control study

<p>Abstract</p> <p>Background</p> <p>TGFBR1*6A is a common hypomorphic variant of transforming growth factor β receptor 1 (TGFBR1). TGFBR1*6A is associated with an increased cancer risk, but the association of this polymorphism with osteosarcoma remains unknown. We have measured the frequency of TGFBR1*6A variants in osteosarcoma cases and controls.</p> <p>Methods</p> <p>Our case-control study is based on 168 osteosarcoma patients and 168 age- and gender-matched controls. Blood samples were obtained and the TGFBR1*6A variant determined by PCR amplification and DNA sequencing. The odds ratio (OR) and 95% confidence interval (95% CI) for the TGFBR1*6A polymorphism were calculated by unconditional logistic regression, adjusted for both age and gender. Three models - dominant, additive and recessive - were used to analyze the contribution of the TGFBR1*6A variant to osteosarcoma susceptibility.</p> <p>Results</p> <p>Heterozygotic and homozygotic TGFBR1*6A variants represented 50.4% and 6.0% of the 168 cases, whereas the controls had 18. 5% and 1.3%, respectively. ORs for homozygosity and heterozygosity of the TGFBR1*6A allele were 4.6 [95% CI, 2.33-7.97] and 2.9 [95% CI, 1.59-5.34] in the additive model. There were significant increases in the TGFBR1*6A variants in osteosarcoma cases compared to control in all 3 models. Further analysis showed that TGFBR1*6A genotypes were not associated with gender, age, or tumor location. However, TGFBR1*6A was significantly associated with less metastasis.</p> <p>Conclusions</p> <p>TGFBR1*6A, a dominant polymorphism of TGFBR1, is associated with increased susceptibility and metastasis spread of osteosarcoma.</p