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

Anomalous behaviors of the charge and spin degrees of freedom in the CuO double chains of PrBa2_2Cu4_4O8_8

The density-matrix renormalization-group method is used to study the electronic states of a two-chain Hubbard model for CuO double chains of PrBa$_2$Cu$_4$O$_8$. We show that the model at quarter filling has the charge ordered phases with stripe-type and in-line--type patterns in the parameter space, and in-between, there appears a wide region of vanishing charge gap; the latter phase is characteristic of either Tomonaga-Luttinger liquid or a metallic state with a spin gap. We argue that the low-energy electronic state of the CuO double chains of PrBa$_2$Cu$_4$O$_8$ should be in the metallic state with a possibly small spin gap.Comment: REVTEX 4, 10 pages, 9 figures; submitted to PR

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