How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey

Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations

Differentially Private Distributed Stochastic Optimization with Time-Varying Sample Sizes

Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differentially private distributed stochastic optimization with time-varying sample sizes are proposed using gradient- and output-perturbation methods. For both gradient- and output-perturbation cases, the convergence of the algorithm and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations are simultaneously established, which is substantially different from the existing works. By a time-varying sample sizes method, the privacy level is enhanced, and differential privacy with a finite cumulative privacy budget $\varepsilon$ for an infinite number of iterations is established. By properly choosing a Lyapunov function, the algorithm achieves almost-sure and mean-square convergence even when the added privacy noises have an increasing variance. Furthermore, we rigorously provide the mean-square convergence rates of the algorithm and show how the added privacy noise affects the convergence rate of the algorithm. Finally, numerical examples including distributed training on a benchmark machine learning dataset are presented to demonstrate the efficiency and advantages of the algorithms

Practices on rockburst prevention and control in headrace tunnels of Jinping II hydropower station

AbstractRockburst problems induced by high in-situ stresses were prominent during construction of the headrace tunnels of Jinping II hydropower station. The rockbursts occurred in various forms, and it is necessary to take pertinent measures for integrated prevention and control of rockbursts. In view of the rockburst characteristics during tunnel construction of Jinping II hydropower station, the engineering geological conditions were presented, and the features, mechanisms and forms of rockbursts observed during construction were analyzed in detail. A large number of scientific researches, experiments and applications were conducted. Multiple measures were adopted to prevent and control rockbursts, including the prediction and early warning measures, stress relief by blasting in advance, optimized blasting design and optimized tunnel support in the tunnel sections prone to strong rockbursts. The effectiveness of these prevention and control measures was evaluated. Experiences have been accumulated through a great number of helpful explorations and practices for rockburst prevention and control. A comprehensive rockburst prevention and control system has been gradually established

Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to preserve local attentive tokens but completely ignore the global token diversity. In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning. According to the class token attention, we decouple the attentive and inattentive tokens. In addition to preserving the most discriminative local tokens, we merge similar inattentive tokens and match homogeneous attentive tokens to maximize the token diversity. Despite its simplicity, our method obtains a promising trade-off between model complexity and classification accuracy. On DeiT-S, our method reduces the FLOPs by 35% with only a 0.2% accuracy drop. Notably, benefiting from maintaining the token diversity, our method can even improve the accuracy of DeiT-T by 0.1% after reducing its FLOPs by 40%

Thermodynamic Analysis of Separating Synchronously Copper and Iron Components from Copper Smelting Slags

AbstractThe occurrence state changes and the possibility of synchronous separation of the components with copper and iron were researched by detailed calculation and derived, from the thermodynamics point of view in the oxidation modification process of copper smelting slag. The relationship between oxygen and sulfur potential of coexistence stage for metallic copper and the magnetite was concluded by analyzed the advantage area chart of Cu-Fe-S-O system. The Thermodynamic studies show that, there was a stability range of the oxygen and sulfur potential in Cu-Fe-S-O system, which made the metallic copper and the magnetite coexistence. The research will provide theoretical support for the synchronous separation between copper and iron components from copper smelting slag