Interconnecting bilayer networks

A typical complex system should be described by a supernetwork or a network of networks, in which the networks are coupled to some other networks. As the first step to understanding the complex systems on such more systematic level, scientists studied interdependent multilayer networks. In this letter, we introduce a new kind of interdependent multilayer networks, i.e., interconnecting networks, for which the component networks are coupled each other by sharing some common nodes. Based on the empirical investigations, we revealed a common feature of such interconnecting networks, namely, the networks with smaller averaged topological differences of the interconnecting nodes tend to share more nodes. A very simple node sharing mechanism is proposed to analytically explain the observed feature of the interconnecting networks.Comment: 9 page

Quantifying quantum non-Markovianity based on quantum coherence via skew information

Based on the nonincreasing property of quantum coherence via skew information under incoherent completely positive and trace-preserving maps, we propose a non-Markovianity measure for open quantum processes. As applications, by applying the proposed measure to some typical noisy channels, we find that it is equivalent to the three previous measures of non-Markovianity for phase damping and amplitude damping channels, i.e., the measures based on the quantum trace distance, dynamical divisibility, and quantum mutual information. For the random unitary channel, it is equivalent to the non-Markovianity measure based on $l_1$ norm of coherence for a class of output states and it is incompletely equivalent to the measure based on dynamical divisibility. We also use the modified Tsallis relative $\alpha$ entropy of coherence to detect the non-Markovianity of dynamics of quantum open systems, the results show that the modified Tsallis relative $\alpha$ entropy of coherence are more comfortable than the original Tsallis relative $\alpha$ entropy of coherence for small $\alpha$.Comment: 13 pages, 5 figure

Noncontact Diffuse Correlation Tomography of Human Breast Tumor

Our first step to adapt our recently developed noncontact diffuse correlation tomography (ncDCT) system for three-dimensional (3-D) imaging of blood flow distribution in human breast tumors is reported. A commercial 3-D camera was used to obtain breast surface geometry, which was then converted to a solid volume mesh. An ncDCT probe scanned over a region of interest on the mesh surface and the measured boundary data were combined with a finite element framework for 3-D image reconstruction of blood flow distribution. This technique was tested in computer simulations and in vivo human breasts with low-grade carcinoma. Results from computer simulations suggest that relatively high accuracy can be achieved when the entire tumor is within the sensitive region of diffuse light. Image reconstruction with a priori knowledge of the tumor volume and location can significantly improve the accuracy in recovery of tumor blood flow contrasts. In vivo imaging results from two breast carcinomas show higher average blood flow contrasts (5.9- and 10.9-fold) in the tumor regions compared to the surrounding tissues, which are comparable with previous findings using diffuse correlation spectroscopy. The ncDCT system has the potential to image blood flow distributions in soft and vulnerable tissues without distorting tissue hemodynamic

Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter

We report a novel noncontact diffuse correlation spectroscopy flow-oximeter for simultaneous quantification of relative changes in tissue blood flow (rBF) and oxygenation (Δ[oxygenation]). The noncontact probe was compared against a contact probe in tissue-like phantoms and forearm muscles (n = 10), and the dynamic trends in both rBF and Δ[oxygenation] were found to be highly correlated. However, the magnitudes of Δ[oxygenation] measured by the two probes were significantly different. Monte Carlo simulations and phantom experiments revealed that the arm curvature resulted in a significant underestimation (~-20%) for the noncontact measurements in Δ[oxygenation], but not in rBF. Other factors that may cause the residual discrepancies between the contact and noncontact measurements were discussed, and further comparisons with other established technologies are needed to identify/quantify these factors. Our research paves the way for noncontact and simultaneous monitoring of blood flow and oxygenation in soft and vulnerable tissues without distorting tissue hemodynamics

Bis(5-amino-4-amino­carbonyl-1H-imid­azol-3-ium) (5-amino-4-amino­carbonyl-1H-imidazol-3-ium-κO)-di-μ-chlorido-hepta­chlorido-dibismuth(III) mono­hydrate

The title compound, (C4H7N4O)2[Bi2Cl9(C4H7N4O)]·H2O, was prepared by the reaction of bis­muth trichloride and 5-amino-1H-imidazole-4-carboxamide in a dilute HCl medium. The asymmetric unit contains two 5-amino-4-amino­carbonyl-1H-imidazol-3-ium cations, one edge-shared non-centrosymmetric biocta­hedral [Bi2C19(C4H7N4O)]2− dianion and a water mol­ecule. In the dianion, the planar 5-amino-4-amino­carbonyl-1H-imidazol-3-ium ligand occupies an equatorial site and is inclined at an angle of 75.7 (2)° to the Bi2(μ-C1)2 plane. The salt forms a three-dimensional network arising from hydrogen bonds between cations, anions and water mol­ecules

SVFAP: Self-supervised Video Facial Affect Perceiver

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding lack of large-scale high-quality labeled data severely hinders their further improvements. Motivated by the recent success of self-supervised learning in computer vision, this paper introduces a self-supervised approach, termed Self-supervised Video Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised methods. Specifically, SVFAP leverages masked facial video autoencoding to perform self-supervised pre-training on massive unlabeled facial videos. Considering that large spatiotemporal redundancy exists in facial videos, we propose a novel temporal pyramid and spatial bottleneck Transformer as the encoder of SVFAP, which not only enjoys low computational cost but also achieves excellent performance. To verify the effectiveness of our method, we conduct experiments on nine datasets spanning three downstream tasks, including dynamic facial expression recognition, dimensional emotion recognition, and personality recognition. Comprehensive results demonstrate that SVFAP can learn powerful affect-related representations via large-scale self-supervised pre-training and it significantly outperforms previous state-of-the-art methods on all datasets. Codes will be available at https://github.com/sunlicai/SVFAP.Comment: Submitted to IEEE Trans. on Affective Computing (February 8, 2023

Alcohol intake and associated risk of major cardiovascular outcomes in women compared with men: a systematic review and meta-analysis of prospective observational studies

Adjustment factors of included studies. Figures S1. RR or RRR (female to male) of low alcohol intake and the risk of coronary disease. Figure S2. RR or RRR (female to male) of moderate alcohol intake and the risk of coronary disease. Figure S3. RR or RRR (female to male) of heavy alcohol intake and the risk of coronary disease. Figure S4. RR or RRR (female to male) of low alcohol intake and the risk of total mortality. Figure S5. RR or RRR (female to male) of moderate alcohol intake and the risk of total mortality. Figure S6. RR or RRR (female to male) of heavy alcohol intake and the risk of total mortality. Figure S7. RR or RRR (female to male) of low alcohol intake and the risk of ischemic stroke. Figure S8. RR or RRR (female to male) of low alcohol intake and the risk of cardiac death. Figure S9. RR or RRR (female to male) of low alcohol intake and the risk of stroke. Figure S10. RR or RRR (female to male) of moderate alcohol intake and the risk of cardiac death. Figure S11. RR or RRR (female to male) of moderate alcohol intake and the risk of stroke. Figure S12. RR or RRR (female to male) of moderate alcohol intake and the risk of ischemic stroke. Figure S13. RR or RRR (female to male) of heavy alcohol intake and the risk of cardiac death. Figure S14. RR or RRR (female to male) of heavy alcohol intake and the risk of stroke. Figure S15. RR or RRR (female to male) of heavy alcohol intake and the risk of ischemic stroke. Figure S16. Funnel plot of RRR (female to male) for low alcohol intake. Figure S17. Funnel plot of RRR (female to male) for moderate alcohol intake. Figure S18. Funnel plot of RRR (female to male) for heavy alcohol intake. (DOC 10344 kb