Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.Comment: Accepted by AAAI 202

Clinical Study Metformin and Diammonium Glycyrrhizinate Enteric-Coated Capsule versus Metformin Alone versus Diammonium Glycyrrhizinate Enteric-Coated Capsule Alone in Patients with Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus

Objective. The present study was conducted to compare the efficacy of metformin combined with diammonium glycyrrhizinate enteric-coated capsule (DGEC) versus metformin alone versus DGEC alone for the treatment of nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM). Subjects and Methods. 163 patients with NAFLD and T2DM were enrolled in this 24-week study and were randomized to one of three groups: group 1 was treated with metformin alone; group 2 was treated with DGEC alone; group 3 received metformin plus DGEC combination therapy. Anthropometric parameters, liver function, lipid profile, serum ferritin (SF), metabolic parameters, liver/spleen computed tomography (CT) ratio, and fibroscan value were evaluated at baseline and after 8, 16, and 24 weeks of treatment. Results. After 24 weeks, significant improvements in all measured parameters were observed in three groups ( < 0.05) except for the improvements in low density lipoprotein cholesterol (LDL-C) and metabolic parameters in group 2 which did not reach statistical significance ( > 0.05). Compared with group 1 and group 2, the patients in group 3 had greater reductions in observed parameters apart from CB and TB ( < 0.05). Conclusions. This study showed that metformin plus DGEC was more effective than metformin alone or DGEC alone in reducing liver enzymes, lipid levels, and metabolic parameters and ameliorating the degree of hepatic fibrosis in patients with NAFLD and T2DM

Direct observation of two-dimensional small polarons at correlated oxide interface

Two-dimensional (2D) perovskite oxide interfaces are ideal systems where diverse emergent properties can be uncovered.The formation and modification of polaronic properties due to short-range strong charge-lattice interactions of 2D interfaces remains hugely intriguing.Here, we report the direct observation of small-polarons at the LaAlO3/SrTiO3 (LAO/STO) conducting interface using high-resolution spectroscopic ellipsometry.First-principles investigations further reveals that strong coupling between the interfacial electrons and the Ti-lattice result in the formation of localized 2D small polarons.These findings resolve the longstanding issue where the excess experimentally measured interfacial carrier density is significantly lower than theoretically predicted values.The charge-phonon induced lattice distortion further provides an analogue to the superconductive states in magic-angle twisted bilayer graphene attributed to the many-body correlations induced by broken periodic lattice symmetry.Our study sheds light on the multifaceted complexity of broken periodic lattice induced quasi-particle effects and its relationship with superconductivity

A Voltage-Based Open-Circuit Fault Detection and Isolation Approach for Modular Multilevel Converters with Model Predictive Control

Fault detection and isolation (FDI) is currently considered a crucial way to increase the reliability of modular multilevel converters (MMCs), which consist of a large number of power electronics submodules (SMs). This paper proposes a fast FDI approach to identifying single open-circuit faults of IGBTs in SMs for MMCs with model predictive control (MPC). The fault detection approach is simply implemented by checking the voltage errors between the measured arm voltages and the estimated ones in the former control cycle. The fault isolation is achieved by checking the switching state directly. The proposed FDI scheme is straightforward and no additional transducer or measurement is required. Compared with the phase-shifted pulse-width modulation (PS-PWM)-based scheme, the MPC has a known and unchanged switching state in a sampling period, which can be utilized for fast location of open-circuit faults. Experimental results show that an open-circuit fault in the MMC can be accurately detected and located in several sampling periods.Accepted versio

Model-predictive current control of modular multilevel converters with phase-shifted pulsewidth modulation

Model-predictive current control (MPCC) is a promising candidate for modular multilevel converter (MMC) control due to its advantages of direct modeling and fast dynamic response. The conventional MPCC, which obtains the optimal control input by evaluating a cost function for all the possible switching states, may make the MPCC impractical due to the exponentially increasing computation burden with the increasing number of submodules (SMs). On the other hand, the MPCC experiences high load current and circulating current tracking errors, since only one switching state is selected and applied during one control period. To address these issues, this paper proposes an MPCC with phase-shifted pulsewidth modulation (PS-PWM) for improving the steady-state control performance. The arm voltages are considered as a whole to implement the proposed MPCC. The optimal duty cycle is obtained based on the load and circulating current tracking error minimization and applied using the PS-PWM. As a result, the computation burden is unrelated to the number of SMs by avoiding the exhaustive evaluation process for all the possible switching states. A better steady-state performance with smaller tracking errors is achieved with the similar switching frequency, and the tedious tuning process of the weighting factor is eliminated. Experimental results are presented to demonstrate the effectiveness of the proposed MPCC