arXiv subjects

Rui Gao

Publications and source records attributed to Rui Gao.

102 records · Page 6Linked to original sources

arXiv2018

Local stable and unstable sets for positive entropy $C^1$ dynamical systems

For any $C^1$ diffeomorphism on a smooth compact Riemannian manifold that admits an ergodic measure with positive entropy, a lower bound of the Hausdorff dimension for the local stable and unstable sets is given in terms of the measure-theoretic entropy and the maximal Lyapunov exponent. The mainline of our approach to this result is under the settings of topological dynamical systems, which is also applicable to infinite dimensional $C^1$ dynamical systems.

math.DS↗

arXiv2018

Testbeam studies of a TORCH prototype detector

TORCH is a novel time-of-flight detector that has been developed to provide charged-particle identification between 2 and 10 GeV/c momentum. TORCH combines arrival times from multiple Cherenkov photons produced within a 10 mm-thick quartz radiator plate, to achieve a 15 ps time-of-flight resolution per incident particle. A customised Micro-Channel Plate photomultiplier tube (MCP-PMT) and associated readout system utilises an innovative charge-sharing technique between adjacent pixels to obtain the necessary 70 ps time resolution of each Cherenkov photon. A five-year R\&D programme has been undertaken, culminating in the construction of a small-scale prototype TORCH module. In testbeams at CERN, this prototype operated successfully with customised electronics and readout system. A full analysis chain has been developed to reconstruct the data and to calibrate the detector. Results are compared to those using a commercial Planacon MCP-PMT, and single photon resolutions approaching 80 ps have been achieved. The photon counting efficiency was found to be in reasonable agreement with a GEANT4 Monte Carlo simulation of the detector. The small-scale demonstrator is a precursor to a full-scale TORCH module (with a radiator plate of $660\times1250\times10~{\rm mm^3}$), which is currently under construction.

physics.ins-det↗

arXiv2018

Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.

stat.ML↗

arXiv2017

Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating

In this paper, we propose a risk-based data-driven approach to optimal power flow (DROPF) with dynamic line rating. The risk terms, including penalties for load shedding, wind generation curtailment and line overload, are embedded into the objective function. To hedge against the uncertainties on wind generation data and line rating data, we consider a distributionally robust approach. The ambiguity set is based on second-order moment and Wasserstein distance, which captures the correlations between wind generation outputs and line ratings, and is robust to data perturbation. We show that the proposed DROPF model can be reformulated as a conic program. Considering the relatively large number of constraints involved, an approximation of the proposed DROPF model is suggested, which significantly reduces the computational costs. A Wasserstein distance constrained DROPF and its tractable reformulation are also provided for practical large-scale test systems. Simulation results on the 5-bus, the IEEE 118-bus and the Polish 2736-bus test systems validate the effectiveness of the proposed models.

math.OC↗

arXiv2017

Image Fusion With Cosparse Analysis Operator

The paper addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited robustness in fusion processing. In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms state-of-the-art fusion methods.

cs.CV↗

arXiv2017

Distributionally Robust Stochastic Optimization with Dependence Structure

Distributionally robust stochastic optimization (DRSO) is a framework for decision-making problems under certainty, which finds solutions that perform well for a chosen set of probability distributions. Many different approaches for specifying a set of distributions have been proposed. The choice matters, because it affects the results, and the relative performance of different choices depend on the characteristics of the problems. In this paper, we consider problems in which different random variables exhibit some form of dependence, but the exact values of the parameters that represent the dependence are not known. We consider various sets of distributions that incorporate the dependence structure, and we study the corresponding DRSO problems. In the first part of the paper, we consider problems with linear dependence between random variables. We consider sets of distributions that are within a specified Wasserstein distance of a nominal distribution, and that satisfy a second-order moment constraint. We obtain a tractable dual reformulation of the corresponding DRSO problem. This approach is compared with the traditional moment-based DRSO and Wasserstein-based DRSO with no moment constraints. Numerical experiments suggest that our new formulation has superior out-of-sample performance. In the second part of the paper, we consider problems with various types of rank dependence between random variables, including rank dependence measured by Spearman's footrule distance between empirical rankings, comonotonic distributions, box uncertainty for individual observations, and Wasserstein distance between copulas associated with continuous distributions. We also obtain a dual reformulation of the DRSO problem. A desirable byproduct of the formulation is that it also avoids an issue associated with the one-sided moment constraints in moment-based DRSO problems.

math.OC↗

arXiv2014

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.

cs.AI↗

arXiv2014

Identifying Social Satisfaction from Social Media

We demonstrate the critical need to identify social situation and instability factors by acquiring public social satisfaction in this research. However, subject to the large amount of manual work cost in subject recruitment and data processing, conventional self-reported method cannot be implemented in real time or applied in large scale investigation. To solve the problem, this paper proposed an approach to predict users' social satisfaction, especially for the economy-related satisfaction based on users' social media records. We recruited 2,018 Cantonese active participants from each city in Guangdong province according to the population distribution. Both behavioral and linguistic features of the participants are extracted from the online records of social media, i.e., Sina Weibo. Regression models are used to predict Sina Weibo users' social satisfaction. Furthermore, we consult the economic indexes of Guangdong in 2012, and calculate the correlations between these indexes and the predicted social satisfaction. Results indicate that social satisfaction can be significantly expressed by specific social media features; local economy satisfaction has significant positive correlations with several local economy indexes, which supports that it is reliable to predict social satisfaction from social media.

cs.CY↗

arXiv2012

Analytic skew-products of quadratic polynomials over Misiurewicz-Thurston maps

We consider skew-products of quadratic maps over certain Misiurewicz-Thurston maps and study their statistical properties. We prove that, when the coupling function is a polynomial of odd degree, such a system admits two positive Lyapunov exponents almost everywhere and a unique absolutely continuous invariant probability measure.

math.DS↗

arXiv2010

ISIS2: Pixel Sensor with Local Charge Storage for ILC Vertex Detector

ISIS (In-situ Storage Imaging Sensor) is a novel CMOS sensor with multiple charge storage capability developed for the ILC vertex detector by the Linear Collider Flavour Identification (LCFI) collaboration. This paper reports test results for ISIS2, the second generation of ISIS sensors implemented in a 0.18 micron CMOS process. The local charge storage and charge transfer were unambiguously demonstrated.

physics.ins-det↗

arXiv2009

Comparison of Measurements of Charge Transfer Inefficiencies in a CCD with High-Speed Column Parallel Readout

Charge Coupled Devices (CCDs) have been successfully used in several high energy physics experiments over the past two decades. Their high spatial resolution and thin sensitive layers make them an excellent tool for studying short-lived particles. The Linear Collider Flavour Identification (LCFI) Collaboration has been developing Column-Parallel CCDs for the vertex detector of a future Linear Collider which can be read out many times faster than standard CCDs. The most recent studies are of devices designed to reduce both the CCD's intergate capacitance and the clock voltages necessary to drive it. A comparative study of measured Charge Transfer Inefficiency values between our previous and new results for a range of operating temperatures is presented.

physics.ins-det↗

arXiv2009

The LCFIVertex package: vertexing, flavour tagging and vertex charge reconstruction with an ILC vertex detector

The precision measurements envisaged at the International Linear Collider (ILC) depend on excellent instrumentation and reconstruction software. The correct identification of heavy flavour jets, placing unprecedented requirements on the quality of the vertex detector, will be central for the ILC programme. This paper describes the LCFIVertex software, which provides tools for vertex finding and for identification of the flavour and charge of the leading hadron in heavy flavour jets. These tools are essential for the ongoing optimisation of the vertex detector design for linear colliders such as the ILC. The paper describes the algorithms implemented in the LCFIVertex package, as well as the scope of the code and its performance for a typical vertex detector design.

physics.ins-det↗