Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples. A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base classes with sufficient examples, where how to decide the transfer weights from base classes to novel classes is the key. However, principled approaches for learning the transfer weights have not been carefully studied. To this end, we propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes, which is built upon a hierarchical Optimal Transport (H-OT) framework. By minimizing the high-level OT distance between novel samples and base classes, we can view the learned transport plan as the adaptive weight information for transferring the statistics of base classes. The learning of the cost function between a base class and novel class in the high-level OT leads to the introduction of the low-level OT, which considers the weights of all the data samples in the base class. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches and owns desired cross-domain generalization ability, indicating the effectiveness of the learned adaptive weights

Enhanced treatment of shale gas fracturing waste fluid through plant-microbial synergism

Embargo until February 12, 2022Cost-efficient and environmentally friendly treatment of hydraulic fracturing effluents is of great significance for the sustainable development of shale gas exploration. We investigated the synergistic effects of plant-microbial treatment of shale gas fracturing waste fluid. The results showed that illumination wavelength and temperature are direct drivers for microbial treatment effects of CODCr and BOD5, while exhibit little effects on nitrogen compounds, TDS, EC, and SS removals as well as microbial species and composition. Plant-microbial synergism could significantly enhance the removal of pollutants compared with removal efficiency without plant enhancement. Additionally, the relative abundance and structure of microorganisms in the hydraulic fracturing effluents greatly varied with the illumination wavelength and temperature under plant-microbial synergism. 201.24 g water dropwort and 435 mg/L activated sludge with illumination of 450–495 nm (blue) at 25 °C was proved as the best treatment condition for shale gas fracturing waste fluid samples, which showed the highest removal efficiency of pollutants and the lowest algal toxicity in treated hydraulic fracturing effluents. The microbial community composition (36.73% Flavobacteriia, 25.01% Gammaproteobacteria, 18.55% Bacteroidia, 9.3% Alphaproteobacteria, 4.1% Cytophagia, and 2.83% Clostridia) was also significantly different from other treatments. The results provide a potential technical solution for improved treatment of shale gas hydraulic fracturing effluents.acceptedVersio

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.Comment: Please feel free to email He-Liang Huang with any comments, questions, suggestions or concern