Contour projected dimension reduction

In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and generalized contour subspace. We show that both of their structural dimensions are no larger than that of the central subspace Cook [Regression Graphics (1998b) Wiley]. Furthermore, we employ CP-sliced inverse regression, CP-sliced average variance estimation and CP-directional regression to estimate the generalized contour subspace, and we subsequently obtain their theoretical properties. Monte Carlo studies demonstrate that the three CP-based dimension reduction methods outperform their corresponding non-CP approaches when the predictors have heavy-tailed elliptical distributions. An empirical example is also presented to illustrate the usefulness of the CP method.Comment: Published in at http://dx.doi.org/10.1214/08-AOS679 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Correlations between aesthetic preferences of river and landscape characters

Some landscape characters put great influences on the aesthetic preferences of a river. Finding out these characters will provide for river landscape design and management with explicit keystones. In this paper, 23 sample areas of rivers were selected in Xuzhou, China, and 15 landscape characters of rivers were identified. The photos taken at the sample areas were as stimuli, and undergraduate students were respondents. The results demonstrate that the aesthetic preferences of photos judged one-by-one and judged together receive similar results; the preference scores of deflective views are significantly higher than the ones of opposite views; for urban rivers, “river accessibility” and “number of colours” are reliably positive predictors to aesthetic preferences, “wood diversity index” and “plants on water” are negative ones; for rural rivers, “coverage of riparian vegetation”, “perspective” and “wood diversity index” are reliably positive predictors to aesthetic preferences. First published online: 14 Dec 201

STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph

The key to traffic prediction is to accurately depict the temporal dynamics of traffic flow traveling in a road network, so it is important to model the spatial dependence of the road network. The essence of spatial dependence is to accurately describe how traffic information transmission is affected by other nodes in the road network, and the GNN-based traffic prediction model, as a benchmark for traffic prediction, has become the most common method for the ability to model spatial dependence by transmitting traffic information with the message passing mechanism. However, existing methods model a local and static spatial dependence, which cannot transmit the global-dynamic traffic information (GDTi) required for long-term prediction. The challenge is the difficulty of detecting the precise transmission of GDTi due to the uncertainty of individual transport, especially for long-term transmission. In this paper, we propose a new hypothesis\: GDTi behaves macroscopically as a transmitting causal relationship (TCR) underlying traffic flow, which remains stable under dynamic changing traffic flow. We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence. To model global transmission, we model the causal order and causal lag of TCRs global transmission by a spatial-temporal alignment algorithm. To capture dynamic spatial dependence, we approximate the stable TCR underlying dynamic traffic flow by a Granger causality test. The experimental results on three backbone models show that using STGC to model the spatial dependence has better results than the original model for 45 min and 1 h long-term prediction.Comment: 14 pages, 16 figures, 4 table

Distinct Effects of IL-18 on the Engraftment and Function of Human Effector CD8+ T Cells and Regulatory T Cells

IL-18 has pleotropic effects on the activation of T cells during antigen presentation. We investigated the effects of human IL-18 on the engraftment and function of human T cell subsets in xenograft mouse models. IL-18 enhanced the engraftment of human CD8+ effector T cells and promoted the development of xenogeneic graft versus host disease (GVHD). In marked contrast, IL-18 had reciprocal effects on the engraftment of CD4+CD25+Foxp3+ regulatory T cells (Tregs) in the xenografted mice. Adoptive transfer experiments indicated that IL-18 prevented the suppressive effects of Tregs on the development of xenogeneic GVHD. The IL-18 results were robust as they were observed in two different mouse strains. In addition, the effects of IL-18 were systemic as IL-18 promoted engraftment and persistence of human effector T cells and decreased Tregs in peripheral blood, peritoneal cavity, spleen and liver. In vitro experiments indicated that the expression of the IL-18Rα was induced on both CD4 and CD8 effector T cells and Tregs, and that the duration of expression was less sustained on Tregs. These preclinical data suggest that human IL-18 may have use as an adjuvant for immune reconstitution after cytotoxic therapies, and to augment adoptive immunotherapy, donor leukocyte infusions, and vaccine strategies