Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization

We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods. Besides the local majorization condition, we only require that the difference between the directional derivatives of the objective function and its surrogate function vanishes when the number of iterations approaches infinity, which is a very weak condition. So our method can use a surrogate function that directly approximates the non-smooth objective function. In comparison, all the existing MM methods construct the surrogate function by approximating the smooth component of the objective function. We apply our relaxed MM methods to the robust matrix factorization (RMF) problem with different regularizations, where our locally majorant algorithm shows advantages over the state-of-the-art approaches for RMF. This is the first algorithm for RMF ensuring, without extra assumptions, that any limit point of the iterates is a stationary point.Comment: AAAI1

Relative Earnings of Husbands and Wives in Urban China

This paper studies the relative contribution of husbands and wives to the family income in the process of economic transition by using the Chinese Urban Household Survey data from 1988 to 1999. We find that, contrary to the experience of western countries, the share of wives¡¦ labor earnings in urban China tends to decline slightly over time and the share of husbands¡¦ labor earnings is stable. This implies that the role of urban Chinese husbands as the main financial supporters of their families becomes relatively more important during economic transition. We argue that this trend may have reflected the restoration of the functions of household production and labor market in the process of economic transition. This restoration allows households to allocate time, effort and human capital investment for each household member and for each household and market activity in a more efficient way. Our further empirical analysis suggests that at least two factors have accounted for the strengthening of the relative importance of husbands in contributing to family income in urban China: 1) the enlargement of the positive effect of children on husbands and the opposite effect for wives; and 2) the shrinkage of the positive income effect on the leisure of husbands.

Turing–Hopf bifurcation and spatiotemporal patterns in a Gierer–Meinhardt system with gene expression delay

In this paper, we consider the dynamics of delayed Gierer–Meinhardt system, which is used as a classic example to explain the mechanism of pattern formation. The conditions for the occurrence of Turing, Hopf and Turing–Hopf bifurcation are established by analyzing the characteristic equation. For Turing–Hopf bifurcation, we derive the truncated third-order normal form based on the work of Jiang et al. [11], which is topologically equivalent to the original equation, and theoretically reveal system exhibits abundant spatial, temporal and spatiotemporal patterns, such as semistable spatially inhomogeneous periodic solutions, as well as tristable patterns of a pair of spatially inhomogeneous steady states and a spatially homogeneous periodic solution coexisting. Especially, we theoretically explain the phenomenon that time delay inhibits the formation of heterogeneous steady patterns, found by S. Lee, E. Gaffney and N. Monk [The influence of gene expression time delays on Gierer–Meinhardt pattern formation systems, Bull. Math. Biol., 72(8):2139–2160, 2010.

Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge

Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data is a common problem in practical applications. The Visual Inductive Priors(VIPriors) Instance Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient Computer Vision Challenges ask competitors to train models from scratch in a data-deficient setting, but there are some visual inductive priors that can be used. In order to address the VIPriors instance segmentation problem, we designed a Task-Specific Data Augmentation(TS-DA) strategy and Inference Processing(TS-IP) strategy. The main purpose of task-specific data augmentation strategy is to tackle the data-deficient problem. And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy. We demonstrate the applicability of proposed method on VIPriors Instance Segmentation Challenge. The segmentation model applied is Hybrid Task Cascade based detector on the Swin-Base based CBNetV2 backbone. Experimental results demonstrate that proposed method can achieve a competitive result on the test set of 2022 VIPriors Instance Segmentation Challenge, with 0.531 [email protected]:0.95.Comment: The first place solution for ECCV 2022 VIPriors Instance Segmentation Challenge. arXiv admin note: text overlap with arXiv:2209.1389

Observer design based on nonlinear suspension model with unscented Kalman filter

This paper presents a new approach to estimating suspension state information and parameter in real-time. An observer with unscented Kalman filter is designed based on a nonlinear quarter car model. The proposed observer could estimate the sprung mass, vertical velocity of sprung and unsprung mass for the nonlinear suspension systems with vehicle load variation. The designed observer has low sensitivity and robust to unknown road surfaces. The efficiency of the estimator is validated through the simulations with two different types of road excitation and payload variations. The simulation results clearly indicate that compared with the extended Kalman filter estimator, the unscented Kalman filter is more accurate and robust. The estimated state information and parameters could be used in the design of suspension control systems

State observer based adaptive sliding mode control for semi-active suspension systems

In order to improve ride comfort and handling stability of a vehicle, this paper will present an adaptive sliding mode control algorithm for semi-active suspension systems. A hybrid reference model is proposed which combines virtues of sky-hook and ground-hook control logics, and chooses a more suitable compromise for a given application. The stability of the adaptive sliding mode control strategy is analyzed by means of Lypunov function approach taking into account the nonlinear damper characteristics and sprung mass variation of the vehicle. A state observer is designed based on unscented Kalman filter to estimate the suspension states in real-time for the realization of the controller, which improves the robustness of the control strategy and is adaptive to different types of road profiles. Finally, the performances of the controller are validated under the following two typical road profiles: the random road and half-sine speed bump road. The simulation results show that the proposed control algorithm can offer a good coordination between ride comfort and handling stability of a vehicle

SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.Comment: 15 pages, under review for IEEE Transactions on Circuits and Systems for Video Technolog