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.

Hotspots of anaerobic ammonium oxidation at land–freshwater interfaces

Abstract: Anammox, anaerobic ammonium oxidation, accounts for over 50% of nitrogen loss in marine ecosystems. A field study in north China reveals hotspots of anammox activity in sediments sampled from land–lake interfaces.

Is Second-Order Information Helpful for Large-Scale Visual Recognition?

Abstract: By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from lowlevel to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-theart works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method consists in covariance pooling, instead of the most commonly used first-order pooling, of highlevel convolutional features. The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices. To address these challenges, we present a Matrix Power Normalized Covariance (MPNCOV) method. We develop forward and backward propagation formulas regarding the nonlinear matrix functions such that MPN-COV can be trained end-to-end. In addition, we analyze both qualitatively and quantitatively its advantage over the well-known Log-Euclidean metric. On the ImageNet 2012 validation set, by combining MPN-COV we achieve over 4%, 3% and 2.5% gains for AlexNet, VGG-M and VGG-16, respectively; integration of MPN-COV into 50-layer ResNet outperforms ResNet-101 and is comparable to ResNet-152. The source code will be available on the project page: http://www.peihuali.org/MPN-COV.

Exploring the impact of COVID-19 on tourism: transformational potential and implications for a sustainable recovery of the travel and leisure industry

Abstract: The study stipulates phases to observe the proposed mechanism in formulating the travel and leisure industry's recovery strategies. The present pandemic COVID-19 has resulted in global challenges, economic and healthcare crises, and posed spillover impacts on the global industries, including tourism and travel that the major contributor to the service industry worldwide. The tourism and leisure industry has faced the COVID-19 tourism impacts hardest-hit and lies among the most damaged global industries. The leisure and internal tourism indicated a steep decline amounting to 2.86 trillion US dollars, which quantified more than 50% revenue losses. In the first step, the study explores the consequences and settings of the COVID-19 pandemic and how innovation and change can contribute to the tourism industry's revival to the next normal. Thus, the study determines that tourism enterprises and scholars must consider and change the basic principles, main assumptions, and organizational situations related to research and practice framework through rebuilding and establishing the tourism sector. In the second step, the study discusses direct COVID-19 tourism impacts, attitudes, and practices in gaining the leisure industry's boom and recovery. In the third phase, the study proposes to observe the characteristics and COVID-19 tourism consequences on the travel and tourism research. The findings provide insights in regaining the tourism industry's operational activities and offer helpful suggestions to government officials, scholars, and tourism firms to reinvest in the tourism industry to set it back to a normal position.