22,347 research outputs found
Supplemental Material - Categorical Confusion: Ideological Labels in China
No full textSupplemental Material for Categorical Confusion: Ideological Labels in China by Jason Y. Wu in Political Research Quarterly</p
JTP833190-APPENDIX – Supplemental material for A spatial valence model of political participation in China
No full textSupplemental material, JTP833190-APPENDIX for A spatial valence model of political participation in China by Jason Y Wu in Journal of Theoretical Politics</p
Replication Data for: Categorical Confusion: Ideological Labels in China
No full textReplication Data for "Categorical Confusion: Ideological Labels in China" in Political Research Quarterl
Control and Filtering for Discrete Linear Repetitive Processes with H infty and ell 2--ell infty Performance
No full textRepetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length. On each pass an output, termed the pass profile, is produced which acts as a forcing function on, and hence contributes to, the dynamics of the next pass profile. This can lead to oscillations which increase in amplitude in the pass to pass direction and cannot be controlled by standard control laws. Here we give new results on the design of physically based control laws for the sub-class of so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control. The main contribution is to show how control law design can be undertaken within the framework of a general robust filtering problem with guaranteed levels of performance. In particular, we develop algorithms for the design of an H? and dynamic output feedback controller and filter which guarantees that the resulting controlled (filtering error) process, respectively, is stable along the pass and has prescribed disturbance attenuation performance as measured by and – norms
Smooth Calibration and Decision Making
No full textCalibration requires predictor outputs to be consistent with their Bayesian posteriors. For machine learning predictors that do not distinguish between small perturbations, calibration errors are continuous in predictions, e.g. smooth calibration error [Foster and Hart, 2018], distance to calibration [Błasiok et al., 2023]. On the contrary, decision-makers who use predictions make optimal decisions discontinuously in probabilistic space, experiencing loss from miscalibration discontinuously. Calibration errors for decision-making are thus discontinuous, e.g., Expected Calibration Error [Foster and Vohra, 1997], and Calibration Decision Loss [Hu and Wu, 2024]. Thus, predictors with a low calibration error for machine learning may suffer a high calibration error for decision-making, i.e. they may not be trustworthy for decision-makers optimizing assuming their predictions are correct. It is natural to ask if post-processing a predictor with a low calibration error for machine learning is without loss to achieve a low calibration error for decision-making. In our paper, we show post-processing an online predictor with ε distance to calibration achieves O(√{ε}) ECE and CDL, which is asymptotically optimal. The post-processing algorithm adds noise to make predictions differentially private. The optimal bound from low distance to calibration predictors from post-processing is non-optimal compared with existing online calibration algorithms that directly optimize for ECE and CDL
Fish oil and post-operative atrial fibrillation: a meta-analysis of randomized controlled trials
No full textDariush Mozaffarian, Jason H. Y. Wu, Marcia C. de Oliveira Otto, Chirag M. Sandesara, Robert G. Metcalf, Roberto Latini, Peter Libby, Federico Lombardi, Patrick T. O’Gara, Richard L. Page, Maria G. Silletta, Luigi Tavazzi, Roberto Marchiol
Acoustic radiation due to scattering of T-S wave by the mean-flow distortion induced by steady local suction
No full textSubstantial sound waves can be generated by boundary-layer instability modes when the latter are scattered by a rapid mean-flow distortion. This is a rather generic mechanism and operates when an oncoming T-S wave is scattered by a steady local suction slot. This paper focuses on this problem by extending a recently developed Local Scattering Theory (Wu & Dong, J. Fluid Mech. submitted), where a so-called transmission coefficient, defined as the ratio of the T-S wave amplitude downstream of the scatter to that upstream, is introduced to characterize the effect of a local scatter on boundary-layer instability and transition. As in the earlier work, the mathematical formulation is based on triple-deck formulism, but in order to accommodate the acoustic far field, which was not considered in the paper mentioned, the unsteady terms in the upper deck, which play a leading-order role in radiation, are retained, and the influence of the radiated sound on the near-wall perturbation is included. The upper deck equation for the pressure is the Helmholtz equation rather than the Laplace equation. This leads to a modified pressure-displacement relation, which is coupled with the linearized boundary-layer equations in the lower deck. Discretization of the whole system formulates a generalized eigenvalue problem, which is solved numerically. It is found that suction suppresses oncoming T-S waves, and this effect increases with the suction velocity and the slot width. The directivity is ndependent of the flow parameters only when the Mach number is low. The intensity of the radiated sound in general increases with the frequency, the suction velocity and the width of the suction slot. Interestingly, for O(1) suction velocities, the radiated sound is very weak, indicating that the gain of stabilizing effect does not cause aeroacoustic penalty
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Tim Wu & “The Curse of Bigness”
No full textHost Nicholas Lemann sits down with Tim Wu, author of The Curse of Bigness, to discuss the politics of Louis Brandeis and Theodore Roosevelt as antitrust has reemerged this year as a major issue in the run-up to the 2020 U.S. Presidential Election.
Data files for "How a raindrop gets shattered on biological surfaces"
No full textSeungho Kim, Zixuan Wu, Ehsan Esmaili, Jason J. Dombroskie, Sunghwan Jung. (2020). How a raindrop gets shattered on biological surfaces
Subsidizing research programs with “if” and “when” uncertainty in the face of severe informational constraints
No full textWe study subsidy policies for research programs when firms have private information about the likelihood of project viability, but the government cannot form a unique prior about this likelihood. When the shadow cost of public funds is zero, first-best welfare can be attained as a (belief-free) ex post equilibrium under both monopoly and competition, but it cannot be attained when the shadow cost is positive. However, max-min subsidy policies exist under monopoly and competition and consist of pure matching subsidies. Under a Research and Development (R&D) consortium, the highest max-min matching rate is lower than under competition, and R&D investment intensity is higher.</p
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