Effective mass of phi mesons at finite temperature

The effective mass of phi meson at non-zero temperature is re-examined with an effective chiral Lagrangian. We find that the phi mass decreases with temperature but the effect is small compared to the result obtained from calculations using QCD sum rules. The leading contributions come from kaon loop corrections but vector meson contributions are also important as temperature increases. We discuss consequences of these changes to the phenomena of chiral phase transition in hot hadronic matter.Comment: 11 pages with two figures (not included), LaTe

Trust, Reciprocity, and Guanxi in China: An Experimental Investigation

We examine the influence of social distance on levels of trust and reciprocity in China. Social distance, reflected in the indigenous concept of guanxi, is of central importance to Chinese culture. In Study 1, some participants participated in two financially salient trust games to measure behavior, one with an anonymous classmate and the other with an anonymous, demographically identical nonclassmate. Other participants, drawn from the same population, completed hypothetical surveys to gauge both hypothetical behavior and expectations of others. Social distance effects on actual and hypothetical behavior were statistically consistent. The results together corroborated the hypothesized negative relationship between trust and social distance. However, reciprocity was not responsive to social distance. Study 2 found that affect-based trust, but not cognition-based trust, played a mediating role in the relationship between social distance and interpersonal trust in a hypothetical scenario. We conclude that close guanxi ties in China engender affect-based trust, which is extended to shouren classmates. This is true despite the fact that no more cognition-based trust is placed nor reciprocity received or expected from classmates compared to demographically identical shengren nonclassmates.Experiment; Affect-based Trust; China; Guanxi; Reciprocity; Trust; Social Distance

catena-Poly[[diaqua­strontium]-bis­(μ-2-bromo­benzoato)-κ2 O,O′:O′;κ3 O:O,O′]

The hydro­thermal reaction of SrCO3 and 2-bromo­benzoic acid in CH3OH–H2O afforded the SrII title polymeric complex, [Sr(C7H4BrO2)2(H2O)2]n. Within the coordination sphere, the SrII ion is located on a crystallographic twofold axis, and is coordinated by eight O atoms from two water mol­ecules and four carboxyl­ate groups of 2-bromo­benzoate ligands in an irregular coordination geometry. Two μ3-carboxyl­ate groups of the 2-bromo­benzoate anions bridge two symmetry-related SrII atoms, giving rise to a chain structure extending along [001]. The polymeric chains are connected via O—H⋯O and O—H⋯Br hydrogen bonds inter­actions into a three-dimensional supra­molecular network

Bis(2-fluoro­benzoato-κ2 O,O′)bis­(1,10-phenanthroline-κ2 N,N′)lead(II) dihydrate

In the title compound, [Pb(C7H4FO2)2(C12H8N2)2]·2H2O, the PbII atom is coordinated by four N atoms from two bidentate chelating 1,10-phenanthroline (phen) ligands and four O atoms from two 2-fluoro­benzoate ligands in an irregular polyhedral coordination geometry. Two carboxyl­ate O atoms and one F atom are each disordered over two sites with occupancy factors of 0.60 and 0.40. The dihedral angle between the two phen ligands is 89.9 (1)°. The mean inter­planar distances are alternatively of 3.44 (3) and 3.45 (3) Å, indicating π–π stacking inter­actions between the neighboring phen ligands. In the crystal, O—H⋯O, O—H⋯F and C—H⋯O hydrogen bonds link the complex mol­ecules and uncoordinated water mol­ecules into a supra­molecular network

Bis(2,2′-bipyridine-κ2 N,N′)dibromido­cadmium(II)

In the title complex mol­ecule, [CdBr2(C10H8N2)2], the CdII ion is six-coordinated by two cis-arranged bromide anions and four N atoms of two bidentate 2,2′-bipyridine ligands in a distorted octa­hedral geometry. The dihedral angle formed by the mean planes through the bipyridine ligands is 87.01 (11)°. In the crystal packing, π–π stacking inter­actions [centroid–centroid distances = 3.837 (6) and 3.867 (11) Å] link adjacent complex mol­ecules into chains running parallel to the b axis. The chains are further connected by inter­molecular C—H⋯Br hydrogen bonds into a three-dimensional network

Two-Stage Bagging Pruning for Reducing the Ensemble Size and Improving the Classification Performance

Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single pruning methods, we found that the two-stage pruning methods can furthermore reduce the ensemble size and improve the classification. “AP+DP” method generally performs better than the “DP+AP” method when using four base classifiers: decision tree, Gaussian naive Bayes, K-nearest neighbor, and logistic regression. Moreover, as compared to the traditional bagging, the two-stage method “AP+DP” improved the classification accuracy by 0.88%, 4.06%, 1.26%, and 0.96%, respectively, averaged over 28 datasets under the four base classifiers. It was also observed that “AP+DP” outperformed other three existing algorithms Brag, Nice, and TB assessed on 8 common datasets. In summary, the proposed two-stage pruning methods are simple and promising approaches, which can both reduce the ensemble size and improve the classification accuracy

Communication-Optimal Distributed Dynamic Graph Clustering

We consider the problem of clustering graph nodes over large-scale dynamic graphs, such as citation networks, images and web networks, when graph updates such as node/edge insertions/deletions are observed distributively. We propose communication-efficient algorithms for two well-established communication models namely the message passing and the blackboard models. Given a graph with $n$ nodes that is observed at $s$ remote sites over time $[1,t]$, the two proposed algorithms have communication costs $\tilde{O}(ns)$ and $\tilde{O}(n+s)$ ($\tilde{O}$ hides a polylogarithmic factor), almost matching their lower bounds, $\Omega(ns)$ and $\Omega(n+s)$, respectively, in the message passing and the blackboard models. More importantly, we prove that at each time point in $[1,t]$ our algorithms generate clustering quality nearly as good as that of centralizing all updates up to that time and then applying a standard centralized clustering algorithm. We conducted extensive experiments on both synthetic and real-life datasets which confirmed the communication efficiency of our approach over baseline algorithms while achieving comparable clustering results.Comment: Accepted and to appear in AAAI'1