Anomalous gauge couplings of the Higgs boson at the CERN LHC: Semileptonic mode in WW scatterings

We make a full tree level study of the signatures of anomalous gauge couplings of the Higgs boson at the CERN LHC via the semileptonic decay mode in WW scatterings. Both signals and backgrounds are studied at the hadron level for the Higgs mass in the range 115 GeV to 200 GeV. We carefully impose suitable kinematical cuts for suppressing the backgrounds. To the same sensitivity as in the pure leptonic mode, our result shows that the semileptonic mode can reduce the required integrated luminosity by a factor of 3. If the anomalous couplings in nature are actually larger than the sensitivity bounds shown in the text, the experiment can start the test for an integrated luminosity of 50 inverse fb.Comment: PACS numbers updated. Version published in Phys.Rev.D79,055010(2009

Cavity QED treatment of scattering-induced efficient free-space excitation and collection in high-Q whispering-gallery microcavities

Whispering-gallery microcavity laser possesses ultralow threshold, whereas convenient free-space optical excitation and collection suffer from low efficiencies due to its rotational symmetry. Here we analytically study a three-dimensional microsphere coupled to a nano-sized scatterer in the framework of quantum optics. It is found that the scatterer is capable of coupling light in and out of the whispering-gallery modes (WGMs) without seriously degrading their high-Q properties, while the microsphere itself plays the role of a lens to focus the input beam on the scatterer and vice versa. Our analytical results show that (1) the high-Q WGMs can be excited in free space, and (2) over 50% of the microcavity laser emission can be collected within less than ${1}^{\circ}$. This coupling system holds great potential for low threshold microlasers free of external couplers.Comment: 10 pages, 8 figure

Onsite data processing and monitoring for the Daya Bay Experiment

The Daya Bay Reactor Neutrino Experiment started running on September 23, 2011. The offline computing environment, consisting of 11 servers at Daya Bay, was built to process onsite data. With current computing ability, onsite data processing is running smoothly. The Performance Quality Monitoring system (PQM) has been developed to monitor the detector performance and data quality. Its main feature is the ability to efficiently process multi-data-stream from three experimental halls. The PQM processes raw data files from the Daya Bay data acquisition system, generates and publishes histograms via a graphical web interface by executing the user-defined algorithm modules, and saves the histograms for permanent storage. The fact that the whole process takes only around 40 minutes makes it valuable for the shift crew to monitor the running status of all the sub-detectors and the data quality

Gaussian Differential Privacy on Riemannian Manifolds

We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds. The concept of GDP stands out as a prominent privacy definition that strongly warrants extension to manifold settings, due to its central limit properties. By harnessing the power of the renowned Bishop-Gromov theorem in geometric analysis, we propose a Riemannian Gaussian distribution that integrates the Riemannian distance, allowing us to achieve GDP in Riemannian manifolds with bounded Ricci curvature. To the best of our knowledge, this work marks the first instance of extending the GDP framework to accommodate general Riemannian manifolds, encompassing curved spaces, and circumventing the reliance on tangent space summaries. We provide a simple algorithm to evaluate the privacy budget $\mu$ on any one-dimensional manifold and introduce a versatile Markov Chain Monte Carlo (MCMC)-based algorithm to calculate $\mu$ on any Riemannian manifold with constant curvature. Through simulations on one of the most prevalent manifolds in statistics, the unit sphere $S^d$, we demonstrate the superior utility of our Riemannian Gaussian mechanism in comparison to the previously proposed Riemannian Laplace mechanism for implementing GDP

Online Local Differential Private Quantile Inference via Self-normalization

Based on binary inquiries, we developed an algorithm to estimate population quantiles under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides asymptotically normal estimation with valid inference, resulting in tight confidence intervals without the need for nuisance parameters to be estimated. Our proposed method can be conducted fully online, leading to high computational efficiency and minimal storage requirements with $\mathcal{O}(1)$ space. We also proved an optimality result by an elegant application of one central limit theorem of Gaussian Differential Privacy (GDP) when targeting the frequently encountered median estimation problem. With mathematical proof and extensive numerical testing, we demonstrate the validity of our algorithm both theoretically and experimentally

Changes of Acylating Stimulating Protein (ASP) and Blood Lipid in Patients with Acute Myocardial Infarction

Objective: To study the changes of acylating stimulating protein (ASP) and blood lipid in patients with acute myocardial infarction. Method: There were three groups,25 cases of acute myocardial infarction patients (acute myocardial infarction group), 32 cases of coronary heart disease patients without myocardial infarction (CHD group) and 30 cases of healthy people (control group). They respectively detected the ASP, low density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C), and analyzed the correlation between them. Results: (1) ASP, TG, TC and LDL-C of acute myocardial infarction group and coronary heart disease group were significantly higher than those of control group, while HDL-C was lower than control group, the difference was statistically significant (P < 0.05). (2) TG in coronary heart disease group was higher than that in acute myocardial infarction group, while ASP, TC, LDL-C and HDL-C had no significant difference. Conclusion: ASP and blood lipid are risk factors of CHD, ASP can be used as risk index of CHD. There was no significant difference in plasma ASP between patients with acute myocardial infarction and patients with coronary heart disease without myocardial infarction. ASP cannot be used as a surrogate marker of acute myocardial infarction

M-estimation in Low-rank Matrix Factorization: a General Framework

Many problems in science and engineering can be reduced to the recovery of an unknown large matrix from a small number of random linear measurements. Matrix factorization arguably is the most popular approach for low-rank matrix recovery. Many methods have been proposed using different loss functions, for example the most widely used L_2 loss, more robust choices such as L_1 and Huber loss, quantile and expectile loss for skewed data. All of them can be unified into the framework of M-estimation. In this paper, we present a general framework of low-rank matrix factorization based on M-estimation in statistics. The framework mainly involves two steps: firstly we apply Nesterov's smoothing technique to obtain an optimal smooth approximation for non-smooth loss function, such as L_1 and quantile loss; secondly we exploit an alternative updating scheme along with Nesterov's momentum method at each step to minimize the smoothed loss function. Strong theoretical convergence guarantee has been developed for the general framework, and extensive numerical experiments have been conducted to illustrate the performance of proposed algorithm