Judicious training pattern for superior molecular reorganization energy prediction model

Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.

The Characteristics and Distribution Pattern of Seafloor Sinuous Pockmark Train in the Niger Delta Basin,West Africa

Objective The term ＂pockmark＂ was introduced by King and MacLean （1970） to describe small ＂circular＂ on echosounder records in Nova Scotia. described as circular, near Pockmarks are usually circular or elongated depressions, generally 10--400 m in diameter and 30-50 m in deep. Pockmarks are normally regarded to be manifestations of fluids escape through the seabed. Pockmarks are valuable features on the seafloor and are useful in constraining the hydrodynamics of sedimentary basins. Since then pockmarks have been recognized in many areas around the world. They occur predominantly in fine-grained siliciclastic depositional settings, although a few case studies have been reported in carbonate settings. In this paper we illustrate a suite of fluid escape features, discovered during the course of petroleum exploration on the West Africa continental margin （Fig. 1）. They are particularly of interest to the oil and gas industry because they could be potential indicators of deeply buried hydrocarbon reservoirs, and fluid flow phenomena in the deep water oilfield are important for the safe and efficient exploration, development and production of hydrocarbons in the area.

The Rossby wave train patterns forced by shallower and deeper Tibetan Plateau atmospheric heat-source in summer in a linear baroclinic model

By using a linear baroclinic model(LBM),this study investigates the different Rossby wave train(RWT)patterns associated with the Tibetan Plateau(TP)upper-atmospheric heat source(TPUHS)that is anomalously shallower and deeper in boreal summer.Observational results indicate the different RWT patterns between the developing and decaying periods of synoptic TPUHS events,when the anomalous TPUHS develops from a relatively shallower to a deeper TP heat source.Based on the different vertical heating profiles between these two periods in observation,this study forces the LBM with prescribed TPUHS profiles to mimic a shallower and deeper summer TP heat source.The results show that the atmospheric responses to a shallower and deeper TPUHS do exhibit different RWT patterns that largely resemble those in observation.Namely,corresponding RWT pattern to a shallower TPUHS stretches from the TP to the west coast of America,while that to a deeper TPUHS extends from the TP region to Alaska.

Method to generate training samples for neural network used in target recognition

Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.

Finite Convergence of On-line BP Neural Networks with Linearly Separable Training Patterns

In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.

Application of Artificial Neural Network in Robotic Hybrid Position/Force Control

A hybrid position/force controller is designed for the joint 2 and the joint 3 of thePUMA 560 robot.The hybrid controller includes a multilayered neural network,which canidentify the dynamics of the contacted environment and can optimize the parameters of PIDcontroller.The experimental results show that after having been trained,the robot has sta-ble response to the training patterns and strong adaptive ability to the situation between thepatterns.

Automated Burned Scar Mapping Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper proposes a fully automated methodology for mapping burn scars using Sentinel-2 data. Information extracted from a pair of Sentinel-2 images, one pre-fire and one post-fire, is jointly used to automatically label a set of training patterns via two empirical rules. An initial pixel-based classification is derived using this training set by means of a Support Vector Machine (SVM) classifier. The latter is subsequently smoothed following a multiple spectral-spatial classification (MSSC) approach, which increases the mapping accuracy and thematic consistency of the final burned area delineation. The proposed methodology was tested on six recent wildfire events in Greece, selected to cover representative cases of the Greek ecosystems and to present challenges in burned area mapping. The lowest classification accuracy achieved was 92%, whereas Matthews correlation coefficient (MCC) was greater or equal to 0.85.