Progress of machine learning in geosciences:Preface

In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.

Machine learning in geosciences and remote sensing

Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Adaptive Walking Control of Biped Robots Using Online Trajectory Genera- tion Method Based on Neural Oscillators

This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass （CoM） online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the gen- erated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on ir- regular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.

Genes important for survival or reproduction in Varroa destructor identified by RNAi

The Varroa mite,(Varroa destructor),is the worst threat to honey bee health worldwide.To explore the possibility of using RNA interference to control this pest, we determined the effects of knocking down various genes on Varroa mite survival and reproduction.Double-stranded RNA (dsRNA)of six candidate genes (Da,Pros26S,RpL8, RpL11,RpPO and RpS13)were synthesized and each injected into Varroa mites,then mite survival and reproduction were assessed.Injection of dsRNA for Da (Daughterless)and Pros26S (Gene for proteasome 26S subunit adenosine triphosphatase)caused a significant reduction in mite survival,with 3.57%±1.94% and 30.03%±11.43% mites surviving at 72 h post-inj ection (hpi),respectively.Control mites injected with green fluorescent protein (GFP)-dsRNA showed survival rates of 81.95%±5.03% and 82.36 ±2.81%,respectively. Injections of dsRNA for four other genes (RpL8,RpL11,RpPO and RpS13)did not affect survival significantly,enabling us to assess their effect on Varroa mite reproduction.The number of female offspring per mite was significantly reduced for mites injected with dsRNA of each of these four genes compared to their GFP-dsRNA controls.Knockdown of the target genes was verified by real-time polymerase chain reaction for two genes important for reproduction (RpL8,RpL11)and one gene important for survival (Pros26S). In conclusion,through RNA interference,we have discovered two genes important for mite survival and four genes important for mite reproduction.These genes could be explored as possible targets for the control of Varroa destructor in the future.