Scholars of the U.S. House disagree over the importance of political parties in organizing the legislative process. On the one hand, non-partisan theories stress how congressional organization serves ...members' non-partisan goals. On the other hand, partisan theories argue that the House is organized to serve the collective interests of the majority party. This book advances our partisan theory and presents a series of empirical tests of that theory's predictions (pitted against others). It considers why procedural cartels form, arguing that agenda power is naturally subject to cartelization in busy legislatures. It argues that the majority party has cartelized agenda power in the U.S. House since the adoption of Reed's rules in 1890. The evidence demonstrates that the majority party seizes agenda control at nearly every stage of the legislative process in order to prevent bills that the party dislikes from reaching the floor.
Radau- and Lobatto-type averaged Gauss rules Reichel, Lothar; Spalević, Miodrag M.
Journal of computational and applied mathematics,
February 2024, 2024-02-00, Volume:
437
Journal Article
Peer reviewed
Open access
We describe numerical methods for the construction of interpolatory quadrature rules of Radau and Lobatto types. In particular, we are interested in deriving efficient algorithms for computing ...optimal averaged Gauss–Radau and Gauss–Lobatto type javascript:undefined;quadrature rules. These averaged rules allow us to estimate the quadrature error in Gauss–Radau and Gauss–Lobatto quadrature rules. This is important since the latter rules have higher algebraic degree of exactness than the corresponding Gauss rules, and this makes it possible to construct averaged quadrature rules of higher algebraic degree of exactness than the corresponding “standard” averaged Gauss rules available in the literature.
The need to evaluate Gauss quadrature rules arises in many applications in science and engineering. It often is important to be able to estimate the quadrature error when applying an ℓ-point Gauss ...rule, Gℓ(f), where f is an integrand of interest. Such an estimate often is furnished by applying another quadrature rule, Qk(f), with k>ℓ nodes, and using the difference Qk(f)−Gℓ(f) or its magnitude as an estimate for the quadrature error in Gℓ(f) or its magnitude. The classical approach to estimate the error in Gℓ(f) is to let Qk(f), with k=2ℓ+1, be the Gauss-Kronrod quadrature rule associated with Gℓ(f). However, it is well known that the Gauss-Kronrod rule associated with a Gauss rule Gℓ(f) might not exist for certain measures that determine the Gauss rule and for certain numbers of nodes. This prompted M. M. Spalević 1 to develop generalized averaged Gauss rules, Gˆ2ℓ+1, with 2ℓ+1 nodes for estimating the error in Gℓ(f). Similarly as for (2ℓ+1)-node Gauss-Kronrod rules, ℓ nodes of the rule Gˆ2ℓ+1 agree with the nodes of Gℓ. However, generalized averaged Gauss rules are not internal for some measures. They therefore may not be applicable when the integrand only is defined on the convex hull of the support of the measure. This paper describes a new kind of quadrature rules that may be internal also when generalized averaged quadrature rules are not. The construction of the new quadrature rules is based on theory developed by Peherstorfer 2. Their application is particularly attractive when the rule Gˆ2ℓ+1 is not internal, the integrand cannot be evaluated at all its nodes, and the integrand is inexpensive to evaluate at the quadrature points. Computed examples that illustrate the performance of the new quadrature rules introduced in this paper are presented.
How is the United States able to control the IMF with only 17 per cent of the votes? How are the rules of the global economy made? This book shows how a combination of formal and informal rules ...explains how international organizations really work. Randall W. Stone argues that formal rules apply in ordinary times, while informal power allows leading states to exert control when the stakes are high. International organizations are therefore best understood as equilibrium outcomes that balance the power and interests of the leading state and the member countries. Presenting a new model of institutional design and comparing the IMF, WTO, and EU, Stone argues that institutional variations reflect the distribution of power and interests. He shows that US interests influence the size, terms, and enforcement of IMF programs, and new data, archival documents, and interviews reveal the shortcomings of IMF programs in Mexico, Russia, Korea, Indonesia, and Argentina.
Based on our previous QCD sum rule studies on hidden-charm pentaquark states, we discuss possible interpretations of the Pc(4312), Pc(4440), and Pc(4457), which were recently observed by LHCb. Our ...results suggest that the Pc(4312) can be well interpreted as the Σc++D¯− bound state with JP=1/2−, while the Pc(4440) and Pc(4457) can be interpreted as the Σc+D¯0 bound state with JP=1/2−, the Σc*++D¯− and Σc+D¯*0 bound states with JP=3/2−, or the Σc*+D¯*0 bound state with JP=5/2−. We propose to measure their spin-parity quantum numbers to verify these assignments.
Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in sparse rule-based systems (and also for reducing the complexity of fuzzy models). However, requirements of ...fuzzy systems may change over time and hence, the use of a static rule base may affect the accuracy of FRI applications. Fortunately, an FRI system in action will produce interpolated rules in abundance during the interpolative reasoning process. While such interpolated results are discarded in existing FRI systems, they can be utilized to facilitate the development of a dynamic rule base in supporting subsequent inference. This is because the otherwise relinquished interpolated rules may contain possibly valuable information, covering regions that were uncovered by the original sparse rule base. This paper presents a dynamic fuzzy rule interpolation (D-FRI) approach by exploiting such interpolated rules in order to improve the overall system's coverage and efficacy. The resulting D-FRI system is able to select, combine, and generalize informative, frequently used interpolated rules for merging with the existing rule base while performing interpolative reasoning. Systematic experimental investigations demonstrate that D-FRI outperforms conventional FRI techniques, with increased accuracy and robustness. Furthermore, D-FRI is herein applied for network security analysis, in devising a dynamic intrusion detection system (IDS) through integration with the Snort software, one of the most popular open source IDSs. This integration, denoted as D-FRI-Snort hereafter, delivers an extra amount of intelligence to predict the level of potential threats. Experimental results show that with the inclusion of a dynamic rule base, by generalising newly interpolated rules based on the current network traffic conditions, D-FRI-Snort helps reduce both false positives and false negatives in intrusion detection.
In this addendum to Ref. 1, we update our results to include the recent measurement of R(D) and R(D*) by the Belle Collaboration 2: R(D)Belle=0.307±0.037±0.016 and R(D*)Belle=0.283±0.018±0.014, ...resulting in the new HFLAV fit result R(D)=0.340±0.027±0.013, R(D*)=0.295±0.011±0.008, exhibiting a 3.1σ tension with the Standard Model. We present the new fit results and update all figures, including the relevant new collider constraints. The updated prediction for R(Λc) from our sum rule reads R(Λc)=RSM(Λc)(1.15±0.04)=0.38±0.01±0.01. We also comment on theoretical predictions for the fragmentation function fc of b→Bc and their implication on the constraint from Bu/c→τν data.
The problem of learning fuzzy rule bases is analyzed from the perspective of finding a favorable balance between the accuracy of the system, the speed required to learn the rules, and, finally, the ...interpretability of the rule bases obtained. Therefore, we introduce a complete design procedure to learn and then optimize the system rule base, called the precise and fast fuzzy modeling approach. Under this paradigm, fuzzy rules are generated from numerical data using a parameterizable greedy-based learning method called selection-reduction , whose accuracy-speed efficiency is confirmed through empirical results and comparisons with reference methods. Qualitative justification for this method is provided based on the coaction between fuzzy logic and the intrinsic properties of greedy algorithms. To complete the precise and fast fuzzy modeling strategy, we finally present a rule-base optimization technique driven by a novel rule redundancy index, which takes into account the concepts of the distance between rules and the influence of a rule over the dataset. Experimental results show that the proposed index can be used to obtain compact rule bases, which remain very accurate, thus increasing system interpretability.
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