Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks

Understanding Lorentz violation with Rashba interaction

Rashba spin orbit interaction is a well studied effect in condensed matter physics and has important applications in spintronics. The Standard Model Extension (SME) includes a CPT-even term with the coefficient H_{\mu \nu} which leads to the Rashba interaction term. From the limit available on the coefficient H_{\mu \nu} in the SME we derive a limit on the Rashba coupling constant for Lorentz violation. In condensed matter physics the Rashba term is understood as resulting from an asymmetry in the confining potential at the interface of two different types of semiconductors. Based on this interpretation we suggest that a possible way of inducing the H_{\mu \nu} term in the SME is with an asymmetry in the potential that confines us to 3 spatial dimensions.Comment: 13 pages, minor corrections. Version to appear in IJMP

Stringy Models of Modified Gravity: Space-time defects and Structure Formation

Starting from microscopic models of space-time foam, based on brane universes propagating in bulk space-times populated by D0-brane defects ("D-particles"), we arrive at effective actions used by a low-energy observer on the brane world to describe his/her observations of the Universe. These actions include, apart from the metric tensor field, also scalar (dilaton) and vector fields, the latter describing the interactions of low-energy matter on the brane world with the recoiling point-like space-time defect (D-particle). The vector field is proportional to the recoil velocity of the D-particle and as such it satisfies a certain constraint. The vector breaks locally Lorentz invariance, which however is assumed to be conserved on average in a space-time foam situation, involving the interaction of matter with populations of D-particle defects. In this paper we demonstrate that, already at the end of the radiation era, the (constrained) vector field associated with the recoil of the defects provides the seeds for a growing mode in the evolution of the Universe. Such a growing mode survives during the matter dominated era, provided the variance of the D-particle recoil velocities on the brane is larger than a critical value.Comment: 30 pages latex, three pdf figures incorporate

The D-material universe

In a previous publication by some of the authors (N.E.M., M.S. and M.F.Y.), we have argued that the "D-material universe", that is a model of a brane world propagating in a higher-dimensional bulk populated by collections of D-particle stringy defects, provides a model for the growth of large-scale structure in the universe via the vector field in its spectrum. The latter corresponds to D-particle recoil velocity excitations as a result of the interactions of the defects with stringy matter and radiation on the brane world. In this article, we first elaborate further on the results of the previous study on the galactic growth era and analyse the circumstances under which the D-particle recoil velocity fluid may "mimic" dark matter in galaxies. A lensing phenomenology is also presented for some samples of galaxies, which previously were known to provide tension for modified gravity (TeVeS) models. The current model is found in agreement with these lensing data. Then we discuss a cosmic evolution for the D-material universe by analysing the conditions under which the late eras of this universe associated with large-scale structure are connected to early epochs, where inflation takes place. It is shown that inflation is induced by dense populations of D-particles in the early universe, with the role of the inflaton field played by the condensate of the D-particle recoil-velocity fields under their interaction with relativistic stringy matter, only for sufficiently large brane tensions and low string mass scales compared to the Hubble scale. On the other hand, for large string scales, where the recoil-velocity condensate fields are weak, inflation cannot be driven by the D-particle defects alone. In such cases inflation may be driven by dilaton (or other moduli) fields in the underlying string theory.Comment: 42 pages latex, one pdf figure incorporated, uses special macro

Crisis intervention for people with severe mental illnesses

Background A particularly difficult challenge for community treatment of people with serious mental illnesses is the delivery of an acceptable level of care during the acute phases of severe mental illness. Crisis-intervention models of care were developed as a possible solution. Objectives To review the effects of crisis-intervention models for anyone with serious mental illness experiencing an acute episode compared to the standard care they would normally receive. If possible, to compare the effects of mobile crisis teams visiting patients' homes with crisis units based in home-like residential houses. Search methods We searched the Cochrane Schizophrenia Group’s Study-Based Register of Trials. There is no language, time, document type, or publication status limitations for inclusion of records in the register. This search was undertaken in 1998 and then updated 2003, 2006, 2010 and September 29, 2014. Selection criteria We included all randomised controlled trials of crisis-intervention models versus standard care for people with severe mental illnesses that met our inclusion criteria. Data collection and analysis We independently extracted data from these trials and we estimated risk ratios (RR) or mean differences (MD), with 95% confidence intervals (CI). We assessed risk of bias for included studies and used GRADE to create a 'Summary of findings' table. Main results The update search September 2014 found no further new studies for inclusion, the number of studies included in this review remains eight with a total of 1144 participants. Our main outcomes of interest are hospital use, global state, mental state, quality of life, participant satisfaction and family burden. With the exception of mental state, it was not possible to pool data for these outcomes. Crisis intervention may reduce repeat admissions to hospital (excluding index admissions) at six months (1 RCT, n = 369, RR 0.75 CI 0.50 to 1.13, high quality evidence), but does appear to reduce family burden (at six months: 1 RCT, n = 120, RR 0.34 CI 0.20 to 0.59, low quality evidence), improve mental state (Brief Psychiatric Rating Scale (BPRS) three months: 2 RCTs, n = 248, MD -4.03 CI -8.18 to 0.12, low quality evidence), and improve global state (Global Assessment Scale (GAS) 20 months; 1 RCT, n = 142, MD 5.70, -0.26 to 11.66, moderate quality evidence). Participants in the crisis-intervention group were more satisfied with their care 20 months after crisis (Client Satisfaction Questionnaire (CSQ-8): 1 RCT, n = 137, MD 5.40 CI 3.91 to 6.89, moderate quality evidence). However, quality of life scores at six months were similar between treatment groups (Manchester Short Assessment of quality of life (MANSA); 1 RCT, n = 226, MD -1.50 CI -5.15 to 2.15, low quality evidence). Favourable results for crisis intervention were also found for leaving the study early and family satisfaction. No differences in death rates were found. Some studies suggested crisis intervention to be more cost-effective than hospital care but all numerical data were either skewed or unusable. We identified no data on staff satisfaction, carer input, complications with medication or number of relapses. Authors' conclusions Care based on crisis-intervention principles, with or without an ongoing homecare package, appears to be a viable and acceptable way of treating people with serious mental illnesses. However only eight small studies with unclear blinding, reporting and attrition bias could be included and evidence for the main outcomes of interest is low to moderate quality. If this approach is to be widely implemented it would seem that more evaluative studies are still neede

Crystal nucleation mechanism in melts of short polymer chains under quiescent conditions and under shear flow

We present a molecular dynamics simulation study of crystal nucleation from undercooled melts of n-alkanes, and we identify the molecular mechanism of homogeneous crystal nucleation under quiescent conditions and under shear flow. We compare results for n-eicosane(C20) and n-pentacontahectane(C150), i.e. one system below the entanglement length and one above. Under quiescent conditions, we observe that entanglement does not have an effect on the nucleation mechanism. For both chain lengths, the chains first align and then straighten locally. Then the local density increases and finally positional ordering sets in. At low shear rates the nucleation mechanism is the same as under quiescent conditions, while at high shear rates the chains align and straighten at the same time. We report on the effects of shear rate and temperature on the nucleation rates and estimate the critical shear rates, beyond which the nucleation rates increase with the shear rate. We show that the viscosity of the system is not affected by the crystalline nuclei.Comment: 9 page

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314