Debiasing Evaluations That are Biased by Evaluations

It is common to evaluate a set of items by soliciting people to rate them. For example, universities ask students to rate the teaching quality of their instructors, and conference organizers ask authors of submissions to evaluate the quality of the reviews. However, in these applications, students often give a higher rating to a course if they receive higher grades in a course, and authors often give a higher rating to the reviews if their papers are accepted to the conference. In this work, we call these external factors the "outcome" experienced by people, and consider the problem of mitigating these outcome-induced biases in the given ratings when some information about the outcome is available. We formulate the information about the outcome as a known partial ordering on the bias. We propose a debiasing method by solving a regularized optimization problem under this ordering constraint, and also provide a carefully designed cross-validation method that adaptively chooses the appropriate amount of regularization. We provide theoretical guarantees on the performance of our algorithm, as well as experimental evaluations

Fatty acid suppression of glial activation prevents central neuropathic pain after spinal cord injury

Supplemental digital content associated with this article can be found online at http://links.lww.com/PAIN/A853Peer reviewedPostprin

Liquid Cell Transmission Electron Microscopy Sheds Light on The Mechanism of Palladium Electrodeposition

Electrodeposition is widely used to fabricate tunable nanostructured materials in applications ranging from biosensing to energy conversion. A model based on 3D island growth is widely accepted in the explanation of the initial stages of nucleation and growth in electrodeposition. However, there are regions in the electrodeposition parameter space where this model becomes inapplicable. We use liquid cell transmission electron microscopy along with post situ scanning electron microscopy to investigate electrodeposition in this parameter space, focusing on the effect of the supporting electrolyte, and to shed light on the nucleation and growth of palladium. Using a collection of electron microscopy images and current time transients recorded during electrodeposition, we discover that electrochemical aggregative growth, rather than 3D island growth, best describes the electrodeposition process. We then use this model to explain the change in the morphology of palladium electrodeposits from spherical to open clusters with nonspherical morphology when HCl is added to the electrolyte solution. The enhanced understanding of the early stages of palladium nucleation and growth and the role of electrolyte in this process provides a systematic route toward the electrochemical fabrication of nanostructured materials

III-V on silicon DFB laser arrays

We will present our work on epitaxially grown III-V on silicon DFB laser arrays, including results of pure InP-based lasers emitting around 900nm and InGaAs-on-InP lasers emitting around 1300nm

Assessing a New Clue to How Much Carbon Plants Take Up

Current climate models disagree on how much carbon dioxide land ecosystems take up for photosynthesis. Tracking the stronger carbonyl sulfide signal could help

Cosmological constraints on the generalized holographic dark energy

We use the Markov ChainMonte Carlo method to investigate global constraints on the generalized holographic (GH) dark energy with flat and non-flat universe from the current observed data: the Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. The most stringent constraints on the GH model parameter are obtained. In addition, it is found that the equation of state for this generalized holographic dark energy can cross over the phantom boundary wde =-1.Comment: 14 pages, 5 figures. arXiv admin note: significant text overlap with arXiv:1105.186

Overview of Strangeness Production at the STAR Experiment

We present an overview of recent STAR results on strangeness production in p+p and heavy-ion collisions at RHIC. In both Cu+Cu and Au+Au collisions we show the centrality dependencies of bulk yield and mid-$p_{T}$ spectrum measurements with new comparisons to theory. The latest $v_{2}$ results for strange particles are presented and prospects for strangeness production in the low energy scan program will be outlined. Finally, we report new measurements of strangeness fragmentation functions for jets in p+p collisions.Comment: 8 pages, 7 figures, proceedings for SQM 200

Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.

Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, Markov chain Monte Carlo sampling of the posterior, computation of the model evidence, and the determination of parameter sensitivities via the Fisher information matrix. Our methodology is implemented in PyRoss, an open-source platform for analysis of epidemiological compartment models