Optical conductivity in the CuO double chains of PrBa_2Cu_4O_8: Consequences of charge fluctuation

We calculate the optical conductivity of the CuO double chains of PrBa$_2$Cu$_4$O$_8$ by the mean-field approximation for the coupled two-chain Hubbard model around quarter filling. We show that the $\sim$40 meV peak structure, spectral shape, and small Drude weight observed in experiment are reproduced well by the present calculation provided that the stripe-type charge ordering presents. We argue that the observed anomalous optical response may be due to the presence of stripe-type fluctuations of charge carriers in the CuO double chains; the fast time scale of the optical measurement should enable one to detect slowly fluctuating order parameters as virtually a long-range order.Comment: 7 pages, 5 eps figure

Proceedings - Asia-Pacific Software Engineering Conference, APSEC

CONTEXT: Several studies in effort estimation havefound that it can be effective to use only recent project data for building an effort estimation model. The generality of this timeaware approach has been explored across a variety of effort estimation model approaches, organizations and definitions of recency. However, other studies have shown that it is not alwayshelpful. A question arises: how can one tell whether the approachwould be effective for a given target project? OBJECTIVE: Toinvestigate a potential method to decide between selecting recentor all project data. METHOD: Using a single-company ISBSGdata set1 studied previously in similar research, we propose andevaluate a selection method. The method utilizes a variant ofcross-validation based on recent projects to make the decision.RESULTS: There are significant differences in the estimation accuracybetween using the proposed method and using the growingportfolio (always using all available data). The method could alsoselect the better approach on average. However, the differencein estimation accuracy between using the proposed method andalways using moving windows was not statistically significant.CONCLUSIONS: The selection method could select the betterapproach on average. The results contribute to developing amethod for suggesting a better approach for practitioners

Empirical Software Engineering in Practice (IWESEP), 2014 6th International Workshop on

BACKGROUND: Several studies in software effortestimation have found that it can be effective to use a window ofrecent projects as training data for building an effort estimationmodel. The previous studies evaluated the use of a windowwith popular estimation models: linear regression (LR) andestimation by analogy (EbA). Many effort estimation models havebeen proposed, and the generality of windowing approach stillremains uncertain for other effort estimation models, especiallyfor those based on different theory. OBJECTIVE: This studyinvestigates the effect of using a window on estimation accuracywith Classification and regression trees (CART). CART wasrecently found as a good performance method, and is based ona different theory from LR and EbA. METHOD: We comparedthe estimation accuracy of a windowing approach and growingapproach with the same data set and procedure as the past stud-ies. RESULTS: There is a difference in the estimation accuracybetween using a window and not using a window. However, theeffctive range of using windows on CART is narrower than thaton LR. CONCLUSIONS: Windowing is also effective with CART.However, the range of effectiveness is narrower. The resultscontribute to the generality of the effectiveness of windowingapproach

On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

In construction of an effort estimation model, it seems effective to use a window of training data so that the model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. In this study, we examined the effects of weighted moving windows on effort estimation accuracy. We compared weighted and non-weighted moving windows under the same experimental settings. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, although the methods also significantly worsened accuracy in smaller windows. This result contributes to understanding properties of moving windows. Copyright © 2014 John Wiley & Sons, Ltd

Up regulated expression of tumour necrosis factor α converting enzyme in peripheral monocytes of patients with early systemic sclerosis

Background: Systemic sclerosis (SSc) is accompanied by abnormalities in humoral and cellular immune systems. Objective: To determine the genes specifically expressed in the immune system in SSc by analysis of the gene expression profile of peripheral blood mononuclear cells (PBMC) from patients with SSc, including those treated with haematopoietic stem cell transplantation (HSCT). Additionally, to investigate the clinical significance of the up regulation of tumour necrosis factor α (TNFα) converting enzyme (TACE). Methods: PBMC from patients with SSc (n = 23) and other autoimmune diseases (systemic lupus erythematosus (SLE, n = 16), rheumatoid arthritis (RA, n = 29)), and from disease-free controls (n = 36) were examined. Complementary DNA arrays were used to evaluate gene expression of PBMC, in combination with real time quantitative polymerase chain reactions. TACE protein expression in PBMC was examined by fluorescence activated cell sorter (FACS). Results: In patients with SSc 118 genes were down regulated after HSCT. Subsequent comparative analysis of SSc without HSCT and healthy controls indicated SSc-specific up regulation for three genes: monocyte chemoattractant protein-3 (p = 0.0015), macrophage inflammatory protein 3α (p = 0.0339), and TACE (p = 0.0251). In the FACS analysis, TACE protein was mainly expressed on CD14(+) monocytes both in patients with SSc and controls. TACE expression on CD14(+) cells was significantly increased in patients with early SSc (p = 0.0096), but not in those with chronic SSc, SLE, or RA. TACE protein levels in SSc monocytes correlated with the intracellular CD68 levels (p = 0.0016). Conclusions: Up regulation of TACE expression was a unique profile in early SSc, and may affect the function of TNFα and other immunoregulatory molecules