Link Prediction via Matrix Completion

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms

A high energy output and low onset temperature nanothermite based on three-dimensional ordered macroporous nano-NiFe2O4

Three-dimensional ordered macroporous (3DOM) Al/NiFe2O4 nanothermite has been obtained by colloidal crystal templating method combined with magnetron sputtering processing. Owing to the superior material properties and unique 3DOM structural characteristics of composite metal oxides, the heat output of the Al/NiFe2O4 nanothermite is up to 2921.7 J g− 1, which is more than the values of Al/NiO and Al/Fe2O3 nanothermites in literature. More importantly, by comparison to the other two nanothermites, the onset temperature of 298.2 °C from Al/NiFe2O4 is remarkably low, which means it can be ignited more easily. Laser ignition experiment indicate that the synthesized Al/NiFe2O4 nanothermite can be easily ignited by laser. In addition, the preparation process is highly compatible with the MEMS technology. These exciting achievements have great potential to expand the scope of nanothermite applications

Toward link predictability of complex networks

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners

Sedentary Behavior Is Independently Related to Fat Mass among Children and Adolescents in South China

We aim to explore the independent associations of sedentary behaviors (SB) with body mass distribution among Chinese children. Data on the screen-based sedentary time (television viewing and computer use) and doing homework, physical activities and dietary intake of 1586 Chinese children (50.3% girls) aged 7–15 years were obtained through validated questionnaires. Skin-fold thickness, body height, and weight were measured to calculate percent body fat (%BF), fat mass index (FMI), and fat-free mass index (FFMI). Parental characteristics were collected by questionnaires. Among girls, time of SB (screen time or doing homework) was positively related to %BF, FMI, and FFMI (p < 0.03) after adjusting for maternal overweight, the average annual income of family, moderate-to-vigorous physical activity energy expenditure, and energy intake: Girls in the highest tertile of screen time/homework had 16.7%/23.3% higher relative FMI and 2.9%/2.9% higher relative FFMI than girls in the lowest tertile. Among boys, screen time was positively associated with FFMI (p 0.09), while time of doing homework was positively related to %BF and FMI (p = 0.03). Sedentary behaviors might be positively and independently related to fat mass among Chinese children, and were more pronounced in girls

A study of two Chinese patients with tetrasomy and pentasomy 15q11q13 including Prader-Willi/Angelman syndrome critical region present with developmental delays and mental impairment

BACKGROUND: The proximal chromosome 15q is prone to unequal crossover, leading to rearrangements. Although 15q11q13 duplications are common in patients with developmental delays and mental impairment, 15q aneusomies resulting in greater or equal to 4 copies of 15q11q13 are rare and no pentasomy 15q11q13 has been reported in the literature. Thus far, all reported high copy number 15q11q13 cases are from the West populations and no such study in Chinese patients have been documented. Dosage-response pattern of high copy number 15q11q13 on clinical presentations is still a subject for further study. CASE PRESENTATION: In this study, we characterized two Han Chinese patients with high copy number 15q11q13. Using chromosome banding, high resolution SNP-based cytogenomic array, Fluorescence in situ hybridization, and PCR-based microsatellite analysis, we identified two patients with tetrasomy 15q11q13 and pentasomy 15q11q13. Both 15q11q13 aneusomies resulted from a maternally inherited supernumerary marker chromosome 15, and each was composed of two different sized 15q11q13 segments covering the Prader-Willi/Angelman critical region: one being about 10 Mb with breakpoints at BP1 and BP5 regions on 15q11 and 15q13, respectively, and another about 8 Mb in size with breakpoints at BP1 and BP4 regions on 15q. Both patients presented with similar clinical features that included neurodevelopmental delays, mental impairment, speech and autistic behavior, and mild dysmorphism. The patient with pentasomy 15q11q13 was more severely affected than the patient with tetrasomy 15q11q13. Low birth weight was noted in patient with pentasomy 15q1q13. CONCLUSIONS: To the best of our knowledge, this is the first case of pentasomy 15q11q13 and the first study of high copy number 15q11q13 in Han Chinese patients. Our findings demonstrate that patients with tetrasomy and pentasomy of chromosome 15q11q13 share similar spectrum of phenotypes reported in other high copy number 15q11q13 patients in the West, and positive correlation between 15q11q13 copy number and degree of severity of clinical phenotypes. Low birth weight observed in the pentasomy 15q11q13 patient was not reported in other patients with high copy number 15q11q13. Additional studies would be necessary to further characterize high copy number 15q11q13 aneusomies

A Dynamical Graph Prior for Relational Inference

Relational inference aims to identify interactions between parts of a dynamical system from the observed dynamics. Current state-of-the-art methods fit a graph neural network (GNN) on a learnable graph to the dynamics. They use one-step message-passing GNNs -- intuitively the right choice since non-locality of multi-step or spectral GNNs may confuse direct and indirect interactions. But the \textit{effective} interaction graph depends on the sampling rate and it is rarely localized to direct neighbors, leading to local minima for the one-step model. In this work, we propose a \textit{dynamical graph prior} (DYGR) for relational inference. The reason we call it a prior is that, contrary to established practice, it constructively uses error amplification in high-degree non-local polynomial filters to generate good gradients for graph learning. To deal with non-uniqueness, DYGR simultaneously fits a ``shallow'' one-step model with shared graph topology. Experiments show that DYGR reconstructs graphs far more accurately than earlier methods, with remarkable robustness to under-sampling. Since appropriate sampling rates for unknown dynamical systems are not known a priori, this robustness makes DYGR suitable for real applications in scientific machine learning