arXiv subjects

Xiaofei Wang

Publications and source records attributed to Xiaofei Wang.

172 records · Page 10Linked to original sources

arXiv2017

Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often

In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common in recent years. Existing literature has documented multiple success stories of teaching statistics with R, bolstered by the capabilities of R Markdown. In this article, we present an in-class data visualization activity intended to expose students to R and R Markdown during the first week of an introductory statistics class. The activity begins with a brief lecture on exploratory data analysis in R. Students are then placed in small groups tasked with exploring a new dataset to produce three visualizations that describe particular insights that are not immediately obvious from the data. Upon completion, students will have produced a series of univariate and multivariate visualizations on a real dataset and practiced describing them.

stat.OT↗

arXiv2017

Enriching students' conceptual understanding of confidence intervals: An interactive trivia-based classroom activity

Confidence intervals provide a way to determine plausible values for a population parameter. They are omnipresent in research articles involving statistical analyses. Appropriately, a key statistical literacy learning objective is the ability to interpret and understand confidence intervals in a wide range of settings. As instructors, we devote a considerable amount of time and effort to ensure that students master this topic in introductory courses and beyond. Yet, studies continue to find that confidence intervals are commonly misinterpreted and that even experts have trouble calibrating their individual confidence levels. In this article, we present a ten-minute trivia game-based activity that addresses these misconceptions by exposing students to confidence intervals from a personal perspective. We describe how the activity can be integrated into a statistics course as a one-time activity or with repetition at intervals throughout a course, discuss results of using the activity in class, and present possible extensions.

stat.OT↗

arXiv2017

Using a "Study of Studies" to help statistics students assess research findings

One learning goal of the introductory statistics course is to develop the ability to make sense of research findings in published papers. The Atlantic magazine regularly publishes a feature called "Study of Studies" that summarizes multiple articles published in a particular domain. We describe a classroom activity to develop this capacity using the "Study of Studies." In this activity, students read capsule summaries of twelve research papers related to restaurants and dining that was published in April 2015. The selected papers report on topics such as how seating arrangement, server posture, plate color and size, and the use of background music relate to revenue, ambiance, and perceived food quality. The students are assigned one of the twelve papers to read and critique as part of a small group. Their group critiques are shared with the class and the instructor. A pilot study was conducted during the 2015-2016 academic year at Amherst College. Students noted that key details were not included in the published summary. They were generally skeptical of the published conclusions. The students often provided additional summarization of information from the journal articles that better describe the results. By independently assessing and comparing the original study conclusions with the capsule summary in the "Study of Studies," students can practice developing judgment and assessing the validity of statistical results.

stat.OT↗

arXiv2016

One-dimensional Arterial Network Model for Bypass Grafts Assessment

We propose an arterial network model based on 1D blood hemodynamic equations to study the behavior of different vascular surgical bypass grafts in case of an arterial occlusive pathology: an obliteration or stenosis of the iliac artery. We investigate the performances of three different bypass grafts (Aorto-Femoral, Axillo-Femoral and cross-over Femoral) depending on the degree of obliteration of the stenosis. Numerical simulations show that all bypass grafts are efficient since we retrieve in all cases the normal hemodynamics in the stenosed region while ensuring at the same time a global healthy circulation. We analyze in particular the Axillo-Femoral bypass graft by performing hundreds of simulations by varying the values of the Young's modulus [0.1--50 MPa] and the radius [0.01--5 cm] of the bypass graft. We show that the Young's modulus and radius of commercial bypass grafts are optimal in terms of hemodynamic considerations. The numerical findings prove that this approach could be used to optimize or plan surgeries for specific patients, to perform parameter's statistics and error propagation evaluations by running extensive simulations, or to characterize numerically specific bypass grafts.

physics.med-ph↗

arXiv2016

Low Rank Approximation of Tensors via Sparse Optimization

The goal of this paper is to find a low-rank approximation for a given tensor. Specifically, we give a computable strategy on calculating the rank of a given tensor, based on approximating the solution to an NP-hard problem. In this paper, we formulate a sparse optimization problem via an $l_1$-regularization to find a low-rank approximation of tensors. To solve this sparse optimization problem, we propose a rescaling algorithm of the proximal alternating minimization and study the theoretical convergence of this algorithm. Furthermore, we discuss the probabilistic consistency of the sparsity result and suggest a way to choose the regularization parameter for practical computation. In the simulation experiments, the performance of our algorithm supports that our method provides an efficient estimate on the number of rank-one tensor components in a given tensor. Moreover, this algorithm is also applied to surveillance videos for low-rank approximation.

math.NA↗

arXiv2016

ABtree: An Algorithm for Subgroup-Based Treatment Assignment

Given two possible treatments, there may exist subgroups who benefit greater from one treatment than the other. This problem is relevant to the field of marketing, where treatments may correspond to different ways of selling a product. It is similarly relevant to the field of public policy, where treatments may correspond to specific government programs. And finally, personalized medicine is a field wholly devoted to understanding which subgroups of individuals will benefit from particular medical treatments. We present a computationally fast tree-based method, ABtree, for treatment effect differentiation. Unlike other methods, ABtree specifically produces decision rules for optimal treatment assignment on a per-individual basis. The treatment choices are selected for maximizing the overall occurrence of a desired binary outcome, conditional on a set of covariates. In this poster, we present the methodology on tree growth and pruning, and show performance results when applied to simulated data as well as real data.

stat.ML↗

arXiv2015

Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge

This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition. In the front-end, Multi-channel Wiener filter (MWF) is designed to achieve background noise reduction. Different from traditional MWF, optimized parameter for the tradeoff between noise reduction and target signal distortion is built according to the desired noise reduction level. In the back-end, several techniques are taken advantage to improve the noisy Automatic Speech Recognition (ASR) performance including Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long short-term memory (LSTM) using medium vocabulary, Lattice rescoring with a big vocabulary language model finite state transducer, and ROVER scheme. Experimental results show the proposed system combining front-end and back-end is effective to improve the ASR performance.

cs.SD↗

arXiv2015

Bayesian Change Point Analysis of Linear Models on Graphs

Consider observations $y_1,\dots,y_n$ on nodes of a connected graph, where the $y_i$ independently come from $N(θ_i, σ^2)$ distributions and an unknown partition divides the $n$ observations into blocks. One well-studied class of change point problems assumes the means $θ_i$ are equal for all nodes within contiguous blocks of a simple graph of sequential observations; both frequentist and Bayesian approaches have been used to estimate the $θ_i$ and the change points of the underlying partition. This paper examines a broad class of change point problems on general connected graphs in which a regression model is assumed to apply within each block of the partition of the graph. This general class also supports multivariate change point problems. We use Bayesian methods to estimate change points or block boundaries of the underlying partition. This paper presents the methodology for the general class of change point problems and develops new algorithms for implementation via Markov Chain Monte Carlo. The paper concludes with simulations and real data examples to demonstrate application of the methodology on a wide range of problems.

stat.ME↗

arXiv2014

Verification and comparison of four numerical schemes for a 1D viscoelastic blood flow model

A reliable and fast numerical scheme is crucial for the 1D simulation of blood flow in compliant vessels. In this paper, a 1D blood flow model is incorporated with a Kelvin-Voigt viscoelastic arterial wall. This leads to a nonlinear hyperbolic-parabolic system, which is then solved with four numerical schemes, namely: MacCormack, Taylor-Galerkin, MUSCL (monotonic upwind scheme for conservation law) and local discontinuous Galerkin. The numerical schemes are tested on a single vessel, a simple bifurcation and a network with 55 arteries. The numerical solutions are checked favorably against analytical, semi-analytical solutions or clinical observations. Among the numerical schemes, comparisons are made in four important aspects: accuracy, ability to capture shock-like phenomena, computational speed and implementation complexity. The suitable conditions for the application of each scheme are discussed.

physics.med-ph↗

arXiv2012

Explosion prediction of oil gas using SVM and Logistic Regression

The prevention of dangerous chemical accidents is a primary problem of industrial manufacturing. In the accidents of dangerous chemicals, the oil gas explosion plays an important role. The essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas. In this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used to predict the explosion of oil gas. LR can get the explicit probability formula of explosion, and the explosive range of the concentrations of oil gas according to the concentration of oxygen. Meanwhile, SVM gives higher accuracy of prediction. Furthermore, considering the practical requirements, the effects of penalty parameter on the distribution of two types of errors are discussed.

cs.CE↗