Residential Greenery in Kvillebäcken. A comparative case study of how residential greenery is perceived in East and West Kvillebäcken

Urban greenery and its importance for city citizens' wellbeing has already been well established in many studies. However, many studies have focused on larger green spaces, such as parks, during summertime, with fully developed greenery. Residential greenery during other times of the year, and people’s perception of it, has not been studied as much. This study intended to fill the research gap by investigating people's perception of residential greenery during spring. This was done through a comparative case study between two areas in Gothenburg, East Kvillebäcken, built according to the Mixed city ideal and West Kvillebäcken, built according to the Nordic functionalism ideal. To make this comparison, a mixed methodology was used, consisting of a face-to-face questionnaire, mental maps and GIS-analysis. The questionnaire asked residents and frequent visitors of each area how they perceived different aspects and photos of the residential greenery, as well as for their favorite green place in their area to create mental maps. The results showed that while the presence of greenery in a residential area was important for the majority, the perception of the residential greenery in each area differed. The residential greenery in the Nordic functionalism typology of West Kvillebäcken was more positively perceived in every aspect regarding amount, accessibility and aesthetics than the Mixed city typology of East Kvillebäcken. Despite this, respondents in both East and West Kvillebäcken still often preferred the nearby parks as their favorite green place, rather than a green place within the residential areas. These findings contribute to the research on subjective evaluation of residential greenery during spring in the two building ideals the Mixed city and the Nordic functionalism

G-Signatures: Global Graph Propagation With Randomized Signatures

Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures. However, GNNs suffer from over-smoothing node information and, therefore, struggle to solve tasks where global graph properties are relevant. We introduce G-Signatures, a novel graph learning method that enables global graph propagation via randomized signatures. G-Signatures use a new graph conversion concept to embed graph structured information which can be interpreted as paths in latent space. We further introduce the idea of latent space path mapping. This allows us to iteratively traverse latent space paths, and, thus globally process information. G-Signatures excel at extracting and processing global graph properties, and effectively scale to large graph problems. Empirically, we confirm the advantages of G-Signatures at several classification and regression tasks.Comment: 7 pages (+ appendix); 4 figure

Measurements of the pp → ZZ production cross section and the Z → 4ℓ branching fraction, and constraints on anomalous triple gauge couplings at √s = 13 TeV

Four-lepton production in proton-proton collisions, pp -> (Z/gamma*)(Z/gamma*) -> 4l, where l = e or mu, is studied at a center-of-mass energy of 13 TeV with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb(-1). The ZZ production cross section, sigma(pp -> ZZ) = 17.2 +/- 0.5 (stat) +/- 0.7 (syst) +/- 0.4 (theo) +/- 0.4 (lumi) pb, measured using events with two opposite-sign, same-flavor lepton pairs produced in the mass region 60 4l) = 4.83(-0.22)(+0.23) (stat)(-0.29)(+0.32) (syst) +/- 0.08 (theo) +/- 0.12(lumi) x 10(-6) for events with a four-lepton invariant mass in the range 80 4GeV for all opposite-sign, same-flavor lepton pairs. The results agree with standard model predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous ZZZ and ZZ. couplings at 95% confidence level: -0.0012 < f(4)(Z) < 0.0010, -0.0010 < f(5)(Z) < 0.0013, -0.0012 < f(4)(gamma) < 0.0013, -0.0012 < f(5)(gamma) < 0.0013

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

IDO1 + Paneth cells promote immune escape of colorectal cancer

Abstract: Tumors have evolved mechanisms to escape anti-tumor immunosurveillance. They limit humoral and cellular immune activities in the stroma and render tumors resistant to immunotherapy. Sensitizing tumor cells to immune attack is an important strategy to revert immunosuppression. However, the underlying mechanisms of immune escape are still poorly understood. Here we discover Indoleamine-2,3-dioxygenase-1 (IDO1)+ Paneth cells in the stem cell niche of intestinal crypts and tumors, which promoted immune escape of colorectal cancer (CRC). Ido1 expression in Paneth cells was strictly Stat1 dependent. Loss of IDO1+ Paneth cells in murine intestinal adenomas with tumor cell-specific Stat1 deletion had profound effects on the intratumoral immune cell composition. Patient samples and TCGA expression data suggested corresponding cells in human colorectal tumors. Thus, our data uncovered an immune escape mechanism of CRC and identify IDO1+ Paneth cells as a target for immunotherapy

IDO1 + Paneth cells promote immune escape of colorectal cancer

Abstract: Tumors have evolved mechanisms to escape anti-tumor immunosurveillance. They limit humoral and cellular immune activities in the stroma and render tumors resistant to immunotherapy. Sensitizing tumor cells to immune attack is an important strategy to revert immunosuppression. However, the underlying mechanisms of immune escape are still poorly understood. Here we discover Indoleamine-2,3-dioxygenase-1 (IDO1)+ Paneth cells in the stem cell niche of intestinal crypts and tumors, which promoted immune escape of colorectal cancer (CRC). Ido1 expression in Paneth cells was strictly Stat1 dependent. Loss of IDO1+ Paneth cells in murine intestinal adenomas with tumor cell-specific Stat1 deletion had profound effects on the intratumoral immune cell composition. Patient samples and TCGA expression data suggested corresponding cells in human colorectal tumors. Thus, our data uncovered an immune escape mechanism of CRC and identify IDO1+ Paneth cells as a target for immunotherapy

Search for dark matter in events with a leptoquark and missing transverse momentum in proton-proton collisions at 13 TeV

A search is presented for dark matter in proton-proton collisions at a center-of-mass energy of root s= 13 TeV using events with at least one high transverse momentum (p(T)) muon, at least one high-p(T) jet, and large missing transverse momentum. The data were collected with the CMS detector at the CERN LHC in 2016 and 2017, and correspond to an integrated luminosity of 77.4 fb(-1). In the examined scenario, a pair of scalar leptoquarks is assumed to be produced. One leptoquark decays to a muon and a jet while the other decays to dark matter and low-p(T) standard model particles. The signature for signal events would be significant missing transverse momentum from the dark matter in conjunction with a peak at the leptoquark mass in the invariant mass distribution of the highest p(T) muon and jet. The data are observed to be consistent with the background predicted by the standard model. For the first benchmark scenario considered, dark matter masses up to 500 GeV are excluded for leptoquark masses m(LQ) approximate to 1400 GeV, and up to 300 GeV for m(LQ) approximate to 1500 GeV. For the second benchmark scenario, dark matter masses up to 600 GeV are excluded for m(LQ) approximate to 1400 GeV. (C) 2019 The Author(s). Published by Elsevier B.V.Peer reviewe