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

Multiscale examination of strain effects in Nd-Fe-B permanent magnets

We have performed a combined first-principles and micromagnetic study on the strain effects in Nd-Fe-B magnets. First-principles calculations on Nd2Fe14B reveal that the magnetocrystalline anisotropy (K) is insensitive to the deformation along c axis and the ab in-plane shrinkage is responsible for the K reduction. The predicted K is more sensitive to the lattice deformation than what the previous phenomenological model suggests. The biaxial and triaxial stress states have a greater impact on K. Negative K occurs in a much wider strain range in the ab biaxial stress state. Micromagnetic simulations of Nd-Fe-B magnets using first-principles results show that a 3-4% local strain in a 2-nm-wide region near the interface around the grain boundaries and triple junctions leads to a negative local K and thus decreases the coercivity by ~60%. The local ab biaxial stress state is more likely to induce a large loss of coercivity. In addition to the local stress states and strain levels themselves, the shape of the interfaces and the intergranular phases also makes a difference in determining the coercivity. Smoothing the edge and reducing the sharp angle of the triple regions in Nd-Fe-B magnets would be favorable for a coercivity enhancement.Comment: 9 figure

Eat, Drink, Firms and Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms

Entertainment and Travel Costs (ETC) is a standard expenditure item for Chinese firms with an annual amount equal to about 20 percent of total wage bills. We use this objective accounting measure as a basis to analyze the composition of ETC and the effect of ETC on firm performance. We rely on the predictions from a simple but plausible model of managerial decision-making to identify components of ETC by examining how the total ETC responds to different environmental variables. In our empirical analysis we find strong evidence that firms. ETC consists of a mix that includes bribery to government officials both as %u201Cgrease money%u201D and %u201Cprotection money,%u201D expenditures to build relational capital with suppliers and clients, and managerial excesses. ETC overall has a significantly negative effect on firm performance, but its negative effect is much less pronounced for those firms located in cities with low quality government service, those who are subject to severe government expropriation, and those who do not have strong relationship with suppliers and clients. Our findings have important implications on how to effectively curb corruption.

A Response-Function-Based Coordination Method for Transmission-Distribution-Coupled AC OPF

With distributed generation highly integrated into the grid, the transmission-distribution-coupled AC OPF (TDOPF) becomes increasingly important. This paper proposes a response-function-based coordination method to solve the TDOPF. Different from typical decomposition methods, this method employs approximate response functions of the power injections with respect to the bus voltage magnitude in the transmission-distribution (T-D) interface to reflect the "reaction" of the distribution to the transmission system control. By using the response functions, only one or two iterations between the transmission system operator (TSO) and the distribution system operator(s) (DSO(s)) are required to attain a nearly optimal TDOPF solution. Numerical tests confirm that, relative to a typical decomposition method, the proposed method does not only enjoy a cheaper computational cost but is workable even when the objectives of the TSO and the DSO(s) are in distinct scales.Comment: This paper will appear at 2018 IEEE PES Transmission and Distribution Conference and Expositio

Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this paper, we take the first attempt for fully unsupervised semantic segmentation of point clouds, which aims to delineate semantically meaningful objects without any form of annotations. Previous works of unsupervised pipeline on 2D images fails in this task of point clouds, due to: 1) Clustering Ambiguity caused by limited magnitude of data and imbalanced class distribution; 2) Irregularity Ambiguity caused by the irregular sparsity of point cloud. Therefore, we propose a novel framework, PointDC, which is comprised of two steps that handle the aforementioned problems respectively: Cross-Modal Distillation (CMD) and Super-Voxel Clustering (SVC). In the first stage of CMD, multi-view visual features are back-projected to the 3D space and aggregated to a unified point feature to distill the training of the point representation. In the second stage of SVC, the point features are aggregated to super-voxels and then fed to the iterative clustering process for excavating semantic classes. PointDC yields a significant improvement over the prior state-of-the-art unsupervised methods, on both the ScanNet-v2 (+18.4 mIoU) and S3DIS (+11.5 mIoU) semantic segmentation benchmarks

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the ``high emission with high price'' principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission

A New Approach to Synthesize of 4-Phenacylideneflavene Derivatives and to Evaluate Their Cytotoxic Effects on HepG2 Cell Line

In this study, a convenient approach and green procedure for the synthesis of 4-phenacylideneflavenes has been developed from the reaction between 2,4-dihydroxybenzaldehyde and substituted acetophenones using boric acid as a catalyst in polyethylene glycol 400. Seven 4-phenacylideneflavenes were synthetized and their structures were confirmed by NMR and mass spectral analyses. Meanwhile, their possible mechanism of formation was also discussed. These products were found to have potential cytotoxic effect on HepG2 cell line with IC50 values from 12.5 to 50 µM

Exploration of muscle fatigue effects in bioinspired robot learning from sEMG signals

© 2018 Ning Wang et al. To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists

Focus on Local Regions for Query-based Object Detection

Query-based methods have garnered significant attention in object detection since the advent of DETR, the pioneering end-to-end query-based detector. However, these methods face challenges like slow convergence and suboptimal performance. Notably, self-attention in object detection often hampers convergence due to its global focus. To address these issues, we propose FoLR, a transformer-like architecture with only decoders. We enhance the self-attention mechanism by isolating connections between irrelevant objects that makes it focus on local regions but not global regions. We also design the adaptive sampling method to extract effective features based on queries' local regions from feature maps. Additionally, we employ a look-back strategy for decoders to retain prior information, followed by the Feature Mixer module to fuse features and queries. Experimental results demonstrate FoLR's state-of-the-art performance in query-based detectors, excelling in convergence speed and computational efficiency