Search for lepton flavor violating decays of a heavy neutral particle in p-pbar collisions at root(s)=1.8 TeV

We report on a search for a high mass, narrow width particle that decays directly to e+mu, e+tau, or mu+tau. We use approximately 110 pb^-1 of data collected with the Collider Detector at Fermilab from 1992 to 1995. No evidence of lepton flavor violating decays is found. Limits are set on the production and decay of sneutrinos with R-parity violating interactions.Comment: Figure 2 fixed. Reference 4 fixed. Minor changes to tex

Fast matrix computations for pair-wise and column-wise commute times and Katz scores

We first explore methods for approximating the commute time and Katz score between a pair of nodes. These methods are based on the approach of matrices, moments, and quadrature developed in the numerical linear algebra community. They rely on the Lanczos process and provide upper and lower bounds on an estimate of the pair-wise scores. We also explore methods to approximate the commute times and Katz scores from a node to all other nodes in the graph. Here, our approach for the commute times is based on a variation of the conjugate gradient algorithm, and it provides an estimate of all the diagonals of the inverse of a matrix. Our technique for the Katz scores is based on exploiting an empirical localization property of the Katz matrix. We adopt algorithms used for personalized PageRank computing to these Katz scores and theoretically show that this approach is convergent. We evaluate these methods on 17 real world graphs ranging in size from 1000 to 1,000,000 nodes. Our results show that our pair-wise commute time method and column-wise Katz algorithm both have attractive theoretical properties and empirical performance.Comment: 35 pages, journal version of http://dx.doi.org/10.1007/978-3-642-18009-5_13 which has been submitted for publication. Please see http://www.cs.purdue.edu/homes/dgleich/publications/2011/codes/fast-katz/ for supplemental code

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

Googling the brain: discovering hierarchical and asymmetric network structures, with applications in neuroscience

Hierarchical organisation is a common feature of many directed networks arising in nature and technology. For example, a well-defined message-passing framework based on managerial status typically exists in a business organisation. However, in many real-world networks such patterns of hierarchy are unlikely to be quite so transparent. Due to the nature in which empirical data is collated the nodes will often be ordered so as to obscure any underlying structure. In addition, the possibility of even a small number of links violating any overall “chain of command” makes the determination of such structures extremely challenging. Here we address the issue of how to reorder a directed network in order to reveal this type of hierarchy. In doing so we also look at the task of quantifying the level of hierarchy, given a particular node ordering. We look at a variety of approaches. Using ideas from the graph Laplacian literature, we show that a relevant discrete optimization problem leads to a natural hierarchical node ranking. We also show that this ranking arises via a maximum likelihood problem associated with a new range-dependent hierarchical random graph model. This random graph insight allows us to compute a likelihood ratio that quantifies the overall tendency for a given network to be hierarchical. We also develop a generalization of this node ordering algorithm based on the combinatorics of directed walks. In passing, we note that Google’s PageRank algorithm tackles a closely related problem, and may also be motivated from a combinatoric, walk-counting viewpoint. We illustrate the performance of the resulting algorithms on synthetic network data, and on a real-world network from neuroscience where results may be validated biologically

Breadwinners and Homemakers: Migration and Changing Conjugal Expectations in Rural Bangladesh

The literature on marriage norms and aspirations across societies largely sees the institution as static – a tool for the assertion of masculinities and subordination of women. The changing meanings of marriage and conjugality in the contemporary context of globalisation have received scant attention. Based on research in rural Bangladesh, this article questions the usefulness of notions of autonomy and dependence in understanding conjugal relations and expectations in a context of widespread migration for extended periods, especially to overseas destinations, where mutuality is crucial for social reproduction, though in clearly genderdemarcated domains

Cost-effectiveness of Implementing Low-Tidal Volume Ventilation in Patients With Acute Lung Injury

Background: Despite widespread guidelines recommending the use of lung-protective ventilation (LPV) in patients with acute lung injury (ALI), many patients do not receive this lifesaving therapy. We sought to estimate the incremental clinical and economic outcomes associated with LPV and determined the maximum cost of a hypothetical intervention to improve adherence with LPV that remained cost-effective. Methods: Adopting a societal perspective, we developed a theoretical decision model to determine the cost-effectiveness of LPV compared to non-LPV care. Model inputs were derived from the literature and a large population-based cohort of patients with ALI. Cost-effectiveness was determined as the cost per life saved and the cost per quality-adjusted life-years (QALYs) gained. Results: Application of LPV resulted in an increase in QALYs gained by 15% (4.21 years for non-LPV vs 4.83 years for LPV), and an increase in lifetime costs of $7,233 per patient with ALI ($99,588 for non-LPV vs $106,821 for LPV). The incremental cost-effectiveness ratios for LPV were$22,566 per life saved at hospital discharge and $11,690 per QALY gained. The maximum, cost-effective, per patient investment in a hypothetical program to improve LPV adherence from 50 to 90$9,482. Results were robust to a wide range of economic and patient parameter assumptions. Conclusions: Even a costly intervention to improve adherence with low-tidal volume ventilation in patients with ALI reduces death and is cost-effective by current societal standards.NIH F32HL090220.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84154/1/Cooke - CEA LPV.pd

International human resource management strategies of Chinese multinationals operating abroad

Strategic international human resource management (SIHRM) is crucial for the effective leveraging of human resources in organizations to achieve the desired business strategies. There is a rich collection of studies on western multinational corporations (MNCs) in China, but few studies that explore the SIHRM of Chinese MNCs operating overseas. This study utilizes cross-level, in-depth interviews to analyse SIHRM of three large Chinese multinationals. The paper contributes to literature by addressing two contextual SIHRM issues, namely the characteristics of the SIHRM for Chinese multinationals and how their SIHRM orientation facilitates their international investment and operation. The findings indicate that organizational transformation is the starting point for latecomers matching their international HRM strategies. Their SIHRM approaches, such as forming learning organizations, reliance on host-country nationals, reconciling both home and host-country effects and promoting ‘best practices’, facilitate their international operations

Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

Background: The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. Methods: A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR). Results: The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH‐VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH‐VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine‐dependent patients from healthy controls. Conclusion: The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm

Exposure to bullying among students with autism spectrum conditions: A multi-informant analysis of risk and protective factors

Research has consistently shown that children and young people with autism spectrum conditions (ASC) are more likely to be bullied than those with other or no special educational needs. The aim of the current study was to examine risk and protective factors that could help to explain variation in exposure to bullying within this group. A sample of 722 teachers and 119 parents reported on their child’s experience of being bullied. This response variable was regressed onto a range of explanatory variables representing individual and contextual factors. The teacher- and parent-rated regression models were statistically significant, explaining large proportions of variance in exposure to bullying. Behaviour difficulties and increased age were associated with bullying in both models. Positive relationships and attending a special school were associated with a decrease in bullying in the teacher model, with use of public/school transport predicting an increase. In the parent model, special educational needs provision at School Action Plus (as opposed to having a Statement of Special Educational Needs) was a significant risk factor, and higher levels of parental engagement and confidence were associated with reductions in bullying. These findings are discussed in relation to the ASC literature, and opportunities for intervention are considered

Electron Charged Graphite-based Hydrogen Storage Material

The electron-charge effects have been demonstrated to enhance hydrogen storage capacity using materials which have inherent hydrogen storage capacities. A charge control agent (CCA) or a charge transfer agent (CTA) was applied to the hydrogen storage material to reduce internal discharge between particles in a Sievert volumetric test device. GTI has tested the device under (1) electrostatic charge mode; (2) ultra-capacitor mode; and (3) metal-hydride mode. GTI has also analyzed the charge distribution on storage materials. The charge control agent and charge transfer agent are needed to prevent internal charge leaks so that the hydrogen atoms can stay on the storage material. GTI has analyzed the hydrogen fueling tank structure, which contains an air or liquid heat exchange framework. The cooling structure is needed for hydrogen fueling/releasing. We found that the cooling structure could be used as electron-charged electrodes, which will exhibit a very uniform charge distribution (because the cooling system needs to remove heat uniformly). Therefore, the electron-charge concept does not have any burden of cost and weight for the hydrogen storage tank system. The energy consumption for the electron-charge enhancement method is quite low or omitted for electrostatic mode and ultra-capacitor mode in comparison of other hydrogen storage methods; however, it could be high for the battery mode