Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

2D Transition Metal Dichalcogenides: Design, Modulation, and Challenges in Electrocatalysis.

Abstract: Hydrogen has been deemed as an ideal substitute fuel to fossil energy because of its renewability and the highest energy density among all chemical fuels One of the most economical, ecofriendly, and high-performance ways of hydrogen production is electrochemical water splitting Recently, 2D transition metal dichalcogenides (also known as 2D TMDs) showed their utilization potentiality as cost-effective hydrogen evolution reaction (HER) catalysts in water electrolysis Herein, recent representative research efforts and systematic progress made in 2D TMDs are reviewed, and future opportunities and challenges are discussed Furthermore, general methods of synthesizing 2D TMDs materials are introduced in detail and the advantages and disadvantages for some specific methods are provided This explanation includes several important regulation strategies of creating more active sites, heteroatoms doping, phase engineering, construction of heterostructures, and synergistic modulation which are capable of optimizing the electrical conductivity, exposure to the catalytic active sites, and reaction energy barrier of the electrode material to boost the HER kinetics In the last section, the current obstacles and future chances for the development of 2D TMDs electrocatalysts are proposed to provide insight into and valuable guidelines for fabricating effective HER electrocatalysts

Enhanced upconversion emission in Yb3+ and Er3+ codoped NaGdF4 nanocrystals by introducing Li+ ions

Abstract: β-NaGdF4 : Yb3+/Er3+ nanoparticles (NPs) codoped with Li+ ions were prepared for the first time via a thermal decomposition reaction of trifluoroacetates in oleylamine The influence of site occupancy of Li+ on the upconversion emission of β-NaGdF4 : Yb3+/Er3+ NPs was investigated in detail The upconversion emission intensity was significantly enhanced by introducing different concentrations of Li+ ions In contrast to lithium-free β-NaGdF4 : Yb3+/Er3+, the green and red UC emission intensities of the NPs codoped with 7 mol% Li+ ions were enhanced by about 47 and 23 times, respectively The luminescence enhancement should be attributed to the distortion of the local asymmetry around Er3+ ions The mechanisms for the enhancement of upconversion emission were discussed In addition, it was found in our research work that β-NaGdF4 : Yb3+/Er3+ NPs exhibited paramagnetic features at room temperature and the magnetization was slightly increased by introducing Li+ ions

Scalable Private Learning with PATE

Abstract: The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfers to a "student" model the knowledge of an ensemble of "teacher" models, with intuitive privacy provided by training teachers on disjoint data and strong privacy guaranteed by noisy aggregation of teachers' answers. However, PATE has so far been evaluated only on simple classification tasks like MNIST, leaving unclear its utility when applied to larger-scale learning tasks and real-world datasets. In this work, we show how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors. For this, we introduce new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Our new mechanisms build on two insights: the chance of teacher consensus is increased by using more concentrated noise and, lacking consensus, no answer need be given to a student. The consensus answers used are more likely to be correct, offer better intuitive privacy, and incur lower-differential privacy cost. Our evaluation shows our mechanisms improve on the original PATE on all measures, and scale to larger tasks with both high utility and very strong privacy ($\varepsilon$ < 1.0).

Nuclear Norm Based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes

Abstract: Recently, regression analysis has become a popular tool for face recognition. Most existing regression methods use the one-dimensional, pixel-based error model, which characterizes the representation error individually, pixel by pixel, and thus neglects the two-dimensional structure of the error image. We observe that occlusion and illumination changes generally lead, approximately, to a low-rank error image. In order to make use of this low-rank structural information, this paper presents a two-dimensional image-matrix-based error model, namely, nuclear norm based matrix regression (NMR), for face representation and classification. NMR uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the regression coefficients. We further develop a fast ADMM algorithm to solve the approximate NMR model and show it has a quadratic rate of convergence. We experiment using five popular face image databases: the Extended Yale B, AR, EURECOM, Multi-PIE and FRGC. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression-based methods for face recognition in the presence of occlusion and illumination variations.

Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory

Abstract: This paper is concerned with developing a distributed ${k}$ -means algorithm and a distributed fuzzy ${c}$ -means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed ${k}$ -means++ algorithm is first proposed to find the initial centroids before executing the distributed ${k}$ -means algorithm and the distributed fuzzy ${c}$ -means algorithm. The proposed distributed ${k}$ -means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy ${c}$ -means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.