A Scalable and Extensible Framework for Superposition-Structured Models

In many learning tasks, structural models usually lead to better interpretability and higher generalization performance. In recent years, however, the simple structural models such as lasso are frequently proved to be insufficient. Accordingly, there has been a lot of work on "superposition-structured" models where multiple structural constraints are imposed. To efficiently solve these "superposition-structured" statistical models, we develop a framework based on a proximal Newton-type method. Employing the smoothed conic dual approach with the LBFGS updating formula, we propose a scalable and extensible proximal quasi-Newton (SEP-QN) framework. Empirical analysis on various datasets shows that our framework is potentially powerful, and achieves super-linear convergence rate for optimizing some popular "superposition-structured" statistical models such as the fused sparse group lasso

Wishart Mechanism for Differentially Private Principal Components Analysis

We propose a new input perturbation mechanism for publishing a covariance matrix to achieve $(\epsilon,0)$-differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, We apply this mechanism to principal component analysis. Our mechanism is able to keep the positive semi-definiteness of the published covariance matrix. Thus, our approach gives rise to a general publishing framework for input perturbation of a symmetric positive semidefinite matrix. Moreover, compared with the classic Laplace mechanism, our method has better utility guarantee. To the best of our knowledge, Wishart mechanism is the best input perturbation approach for $(\epsilon,0)$-differentially private PCA. We also compare our work with previous exponential mechanism algorithms in the literature and provide near optimal bound while having more flexibility and less computational intractability.Comment: A full version with technical proofs. Accepted to AAAI-1

2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

The Characteristics and Outlook on Management Accounting Practice in China

As the managerial and controlling system of enterprise, the objective of management accounting is to help managers to achieve the specific management target by a series of management activities, such as planning, decision-making, control and responsibility performance evaluation. Based on review to the history of China\u27s management accounting practice, the paper points out application depth and using field of management accounting in China, and summarizes successful experience in the application of management accounting in enterprises and reveals current application characteristics of management accounting in China. We find out the main problems on management accounting practice in China and outlook the development direction in the future.第194回公開講

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction

The contribution of this paper includes two aspects. First, we study the lower bound complexity for the minimax optimization problem whose objective function is the average of $n$ individual smooth component functions. We consider Proximal Incremental First-order (PIFO) algorithms which have access to gradient and proximal oracle for each individual component. We develop a novel approach for constructing adversarial problems, which partitions the tridiagonal matrix of classical examples into $n$ groups. This construction is friendly to the analysis of incremental gradient and proximal oracle. With this approach, we demonstrate the lower bounds of first-order algorithms for finding an $\varepsilon$-suboptimal point and an $\varepsilon$-stationary point in different settings. Second, we also derive the lower bounds of minimization optimization with PIFO algorithms from our approach, which can cover the results in \citep{woodworth2016tight} and improve the results in \citep{zhou2019lower}

An implicit Cartesian cut-cell method for incompressible viscous flows with complex geometries

A versatile conservative three-dimensional Cartesian cut-cell method for simulation of incompressible viscous flows over complex geometries is presented in this paper. The present method is based on the finite volume method on a non-uniform staggered grid together with a consistent mass and momentum flux computation. Contrary to the commonly cut-cell methods, an implicit time integration scheme is employed in the present method, which avoids numerical instability without any additional small cut-cell treatment. Strict conservation of the mass and momentum for both fluid and cut cells is enforced through the PISO algorithm for the pressure–velocity coupling. The versatility and robustness of the present cut-cell method are demonstrated by simulating various two- and three-dimensional canonical benchmarks (flow over a circular cylinder, airfoil, sphere, pipe, and heart sculpture) and the computed results agree well with previous experimental measurements and various numerical results obtained from the boundary-fitted, immersed boundary/interface, and other cut-cell methods, verifying the accuracy of the proposed method