A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

Crosswind stability of high-speed trains in special cuts

Analysis of the aerodynamic performance of high-speed trains in special cuts would provide references for the critical overturning velocity and complement the operation safety management under strong winds.This work was conducted to investigate the flow structure around trains under different cut depths,slope angles using computational fluid dynamics(CFD).The high-speed train was considered with bogies and inter-carriage gaps.And the accuracy of the numerical method was validated by combining with the experimental data of wind tunnel tests.Then,the variations of aerodynamic forces and surface pressure distribution of the train were mainly analyzed.The results show that the surroundings of cuts along the railway line have a great effect on the crosswind stability of trains.With the slope angle and depth of the cut increasing,the coefficients of aerodynamic forces tend to reduce.An angle of 75°is chosen as the optimum one for the follow-up research.Under different depth conditions,the reasonable cut depth for high-speed trains to run safely is 3 m lower than that of the conventional cut whose slope ratio is 1:1.5.Furthermore,the windward slope angle is more important than the leeward one for the train aerodynamic performance.Due to the shield of appropriate cuts,the train body is in a minor positive pressure environment.Thus,designing a suitable cut can contribute to improving the operation safety of high-speed trains.

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.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.

Increasing Momentum-Like Factors:A Method for Reducing Training Errors on Multiple GPUs

In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training errors.In this work,we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent(SGD) and Adaptive moment estimation(Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit(GPU).A new method that considers momentum to eliminate training errors in distributed training is proposed.We define a Momentum-like Factor(MF) to represent the influence of former gradients on parameter updates in each iteration.Then,we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD,Adam,and Nesterov accelerated gradient.Experimental results reveal that increasing MFs is a reliable method for reducing training errors in distributed training.The analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.

xCCL:A Survey of Industry-Led Collective Communication Libraries for Deep Learning

Machine learning techniques have become ubiquitous both in industry and academic applications.Increasing model sizes and training data volumes necessitate fast and efficient distributed training approaches.Collective communications greatly simplify inter-and intra-node data transfer and are an essential part of the distributed training process as information such as gradients must be shared between processing nodes.In this paper,we survey the current state-of-the-art collective communication libraries(namely xCCL,including NCCL,oneCCL,RCCL,MSCCL,ACCL,and Gloo),with a focus on the industry-led ones for deep learning workloads.We investigate the design features of these xCCLs,discuss their use cases in the industry deep learning workloads,compare their performance with industry-made benchmarks(i.e.,NCCL Tests and PARAM),and discuss key take-aways and interesting observations.We believe our survey sheds light on potential research directions of future designs for xCCLs.