A Multi-Task Deep Learning Framework for Simultaneous Detection of Thoracic Pathology through Image Classification

Thoracic diseases pose significant risks to an individual's chest health and are among the most perilous medical diseases. They can impact either one or both lungs, which leads to a severe impairment of a person’s ability to breathe normally. Some notable examples of such diseases encompass pneumonia, lung cancer, coronavirus disease 2019 (COVID-19), tuberculosis, and chronic obstructive pulmonary disease (COPD). Consequently, early and precise detection of these diseases is paramount during the diagnostic process. Traditionally, the primary methods employed for the detection involve the use of X-ray imaging or computed tomography (CT) scans. Nevertheless, due to the scarcity of proficient radiologists and the inherent similarities between these diseases, the accuracy of detection can be compromised, leading to imprecise or erroneous results. To address this challenge, scientists have turned to computer-based solutions, aiming for swift and accurate diagnoses. The primary objective of this study is to develop two machine learning models, utilizing single-task and multi-task learning frameworks, to enhance classification accuracy. Within the multi-task learning architecture, two principal approaches exist soft parameter sharing and hard parameter sharing. Consequently, this research adopts a multi-task deep learning approach that leverages CNNs to achieve improved classification performance for the specified tasks. These tasks, focusing on pneumonia and COVID-19, are processed and learned simultaneously within a multi-task model. To assess the effectiveness of the trained model, it is rigorously validated using three different real-world datasets for training and testing.

Serial structure multi-task learning method for predicting reservoir parameters

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.

An efficient microreactor with continuous serially connected micromixers for the synthesis of superparamagnetic magnetite nanoparticles

A new microreactor with continuous serially connected micromixers(CSCM)was tailored for the coprecipitation process to synthesize Fe_(3)O_(4) nanoparticles.Numerical simulation reveals that the two types of CSCM microchannels(V-typed and U-typed)proposed in this work exhibited markedly better mixing performances than the Zigzag and capillary microchannels due to the promotion of Dean vortices.Complete mixing was achieved in the V-typed microchannel in 2.7 s at an inlet Reynolds number of 27.Fe_(3)O_(4) nanoparticles synthesized in a planar glass microreactor with the V-typed microchannel,possessing an average size of 9.3 nm and exhibiting superparamagnetism,had obviously better dispersity and uniformity and higher crystallinity than those obtained in the capillary microreactor.The new CSCM microreactor developed in this work can act as a potent device to intensify the synthesis of similar inorganic nanoparticles via multistep chemical precipitation processes.

Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.

MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN

Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.

Robust multi-task distributed estimation based on generalized maximum correntropy criterion

False data injection(FDI) attacks are common in the distributed estimation of multi-task network environments, so an attack detection strategy is designed by combining the generalized maximum correntropy criterion. Based on this, we propose a diffusion least-mean-square algorithm based on the generalized maximum correntropy criterion(GMCC-DLMS)for multi-task networks. The algorithm achieves gratifying estimation results. Even more, compared to the related work,it has better robustness when the number of attacked nodes increases. Moreover, the assumption about the number of attacked nodes is relaxed, which is applicable to multi-task environments. In addition, the performance of the proposed GMCC-DLMS algorithm is analyzed in the mean and mean-square senses. Finally, simulation experiments confirm the performance and effectiveness against FDI attacks of the algorithm.

The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.

Multi-Task Timing Assignment Algorithm for Intelligent Production of Vegetables in Open Field

Vegetable production in the open field involves many tasks,such as soil preparation,ridging,and transplanting/sowing.Different tasks require agricultural machinery equipped with different agricultural tools to meet the needs of the operation.Aiming at the coupling multi-task in the intelligent production of vegetables in the open field,the task assignment method for multiple unmanned tractors based on consistency alliance is studied.Firstly,unmanned vegetable production in the open field is abstracted as a multi-task assignment model with constraints of task demand,task sequence,and the distance traveled by an unmanned tractor.The tight time constraints between associated tasks are transformed into time windows.Based on the driving distance of the unmanned tractor and the replacement cost of the tools,an expanded task cost function is innovatively established.The task assignment model of multiple unmanned tractors is optimized by the consensus based bundle algorithm(CBBA)with time windows.Experiments show that the method can effectively solve task conflict in unmanned production and optimize task allocation.A basic model is provided for the cooperative task of multiple unmanned tractors for vegetable production in the open field.

Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

Online advertising has gained much attention on various platforms as a hugely lucrative market.In promoting content and advertisements in real life,the acquisition of user target actions is usually a multi-step process,such as impres-sion→click→conversion,which means the process from the delivery of the recommended item to the user’s click to the final conversion.Due to data sparsity or sample selection bias,it is difficult for the trained model to achieve the business goal of the target campaign.Multi-task learning,a classical solution to this pro-blem,aims to generalize better on the original task given several related tasks by exploiting the knowledge between tasks to share the same feature and label space.Adaptively learned task relations bring better performance to make full use of the correlation between tasks.We train a general model capable of captur-ing the relationships between various tasks on all existing active tasks from a meta-learning perspective.In addition,this paper proposes a Multi-task Attention Network(MAN)to identify commonalities and differences between tasks in the feature space.The model performance is improved by explicitly learning the stacking of task relationships in the label space.To illustrate the effectiveness of our method,experiments are conducted on Alibaba Click and Conversion Pre-diction(Ali-CCP)dataset.Experimental results show that the method outperforms the state-of-the-art multi-task learning methods.

Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis(ABSA)is a fine-grained process.Its fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely related.However,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline model.Pipeline solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE error.These methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of APC.Thus,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a singlemodel.This paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text representation.The LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its context.Moreover,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training dataset.This paper examined the effect of data augmentation on the multi-task model for Arabic ABSA.Extensive experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC techniques.Superior results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation word embedding-based method(FastText)on the combined dataset.

Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.

A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.

Vision-based multi-level synthetical evaluation of seismic damage for RC structural components: a multi-task learning approach

Recent studies for computer vision and deep learning-based,post-earthquake inspections on RC structures mainly perform well for specific tasks,while the trained models must be fine-tuned and re-trained when facing new tasks and datasets,which is inevitably time-consuming.This study proposes a multi-task learning approach that simultaneously accomplishes the semantic segmentation of seven-type structural components,three-type seismic damage,and four-type deterioration states.The proposed method contains a CNN-based encoder-decoder backbone subnetwork with skip-connection modules and a multi-head,task-specific recognition subnetwork.The backbone subnetwork is designed to extract multi-level features of post-earthquake RC structures.The multi-head,task-specific recognition subnetwork consists of three individual self-attention pipelines,each of which utilizes extracted multi-level features from the backbone network as a mutual guidance for the individual segmentation task.A synthetical loss function is designed with real-time adaptive coefficients to balance multi-task losses and focus on the most unstably fluctuating one.Ablation experiments and comparative studies are further conducted to demonstrate their effectiveness and necessity.The results show that the proposed method can simultaneously recognize different structural components,seismic damage,and deterioration states,and that the overall performance of the three-task learning models gains general improvement when compared to all single-task and dual-task models.

A General Linguistic Steganalysis Framework Using Multi-Task Learning

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary classification.While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist.In this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts.LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model.In the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis task.Besides,a shared CNN is built to capture potential interaction information and share linguistic features among all tasks.Finally,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic.Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,respectively.More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

基于POE模型的工业机器人运动学参数二次辨识方法研究

针对工业机器人在高度制造领域精度不高的问题,本文提出了一种基于POE模型的工业机器人运动学参数二次辨识方法。阐述了基于指数积(Product of exponential, POE)模型的运动学误差模型构建方法,并建立基于POE误差模型的适应度函数;为实现高精度的参数辨识,提出了一种二次辨识方法,先利用改进灰狼优化算法(Improved grey wolf optimizer, IGWO)实现运动学参数误差的粗辨识,初步将Staubli TX60型机器人的平均位置误差和平均姿态误差分别从(0.648 mm, 0.212°)降低为(0.457 mm, 0.166°);为进一步提高机器人的精度性能,再通过LM(Levenberg-Marquard)算法进行参数误差的精辨识,最终将Staubli TX60型机器人平均位置误差和平均姿态误差进一步降低为(0.237 mm, 0.063°),机器人平均位置误差和平均姿态误差分别降低63.4%和70.2%。为了验证上述二次辨识方法的稳定性,随机选取5组辨识数据集和验证数据集进行POE误差模型的参数误差辨识,结果表明提出的二次辨识方法能够稳定、精确地辨识工业机器人运动学参数误差。

信息技术与企业绩效:知识管理能力与商业模式创新的链式中介作用

信息化时代背景下,企业可以通过先进的信息技术提高管理能力与组织绩效。基于资源基础观和动态能力理论,依循“资源—能力—绩效”的逻辑,以知识管理能力和商业模式创新为中介,探讨信息技术对企业绩效的影响机理。以创新型企业为研究对象,采用问卷调查法收集297份样本数据,并运用偏最小二乘结构方程模型和模糊集定性比较分析方法进行实证检验。结果表明,信息技术对知识管理能力和企业绩效的正向直接效应均显著,知识管理能力对商业模式创新、商业模式创新对企业绩效的正向直接效应均显著,且商业模式创新与知识管理能力在信息技术对企业绩效的正向影响中发挥链式中介作用;企业实现高水平绩效存在3种组态,也间接证实知识管理能力与商业模式创新在信息技术与企业绩效之间存在链式中介效应。研究有助于增进对信息技术与企业绩效间作用关系的理解,丰富资源基础观与动态能力理论,进而为创新型企业可持续成长提供路径指引。

一种轻小型遥感相机多通道高速图像传输方案

遥感相机图像传输接口性能是影响相机整体性能的重要指标,为解决多通道相机图像数据传输速率高且同时满足轻小型化需求的问题,文章提出一种高吞吐率、低误码率的轻小型相机多通道高速图像数据传输方案,该方案基于CoaXPress接口采用Aurora 8B/10B通信协议,通过FPGA吉比特发送器(Gigabit Transceiver,GTX)实现4套焦面组件下行的高速图像传输,测试数据率达到13 Gbit/s,同时实现上行21 Mbit/s的相机控制数据率。仿真及测试结果表明:该方案极大地提高了相机的图像数据传输速率,接口电缆数量较传统减少近50%,可满足轻小型遥感相机多通道高速图像传输需求,为各类遥感相机高速小型化提供新的解决方案。

基于累加式实时串并联变换算法的机械故障声学监测方法

针对基于物联网(IoT)的冲压机床故障监测问题,为了降低冲压机床故障监测的计算复杂度,并提高其低频识别的精度,提出了一种无需机器学习技术的实时性机械故障声学监测方法,即基于累加式实时串并联变换算法的机械故障声学监测方法。首先,研究了物联网场景中冲压机床声学低频分析的必要性,并给出了声学信号的表达式;然后,针对频率轴上多个周期信号重叠导致参数估计较为困难的问题,提出了一种累加式实时串并联变换算法,将输入的采样序列馈入多个具有不同输出端口的串并转换器,从累加的波形中检测出最大绝对值,并进行了比较;最后,通过样本时隙划分,将累加式实时串并联变换算法应用于机械故障监测;通过仿真和冲压机床实机测试,对累加式实时串并联变换算法和实时性机械故障声学监测方法的有效性进行了验证。研究结果表明:在无需大量信号样本的情况下,使用累加式实时串并联变换算法有利于提高低频带的识别精度;在直方图相关性方面,累加式实时串并联变换算法和Morlet小波变换具有相同的性能,且均明显优于短时傅立叶变换;同时,尽管累加式实时串并联变换算法需要的加法总数比Morlet小波变换多2.5倍,但是乘法总数减少了20447%,大幅减少了计算的复杂度。

基于脑电的快速序列视觉呈现脑-机接口系统研究进展综述

脑-机接口(BCI)系统建立大脑与外部设备之间的直接交流通路,结合快速序列视觉呈现(RSVP)范式能够实现利用人类视觉系统进行高流通量图像目标检索。近些年来,RSVP-BCI系统在范式编码、脑电(EEG)解码和系统应用方面的研究取得了长足的进步。对范式编码的研究揭示不同范式参数对系统性能的影响,促进提升系统性能;脑电解码的研究在提升算法分类性能的同时推动少训练、零训练样本、多模态等场景下的应用;对RSVP-BCI系统应用的研究实现推动系统走向实际应用并拓宽了应用领域。同时,系统仍面临着迈向实际时可应用领域范围窄、脑电跨域解码难题以及计算机视觉飞速进步带来的挑战。该文对RSVP-BCI近年来的相关研究进展进行了回顾与总结,并对未来的发展方向进行了展望。

基于SRIO的双备份数据传输

为了解决在特殊条件下数据通道受到外界因素影响导致数据无法传输的问题,提出一种基于SRIO的双备份数据传输设计方案。设计中使用两个独立的SRIO IP核来实现主路通道和备份通道数据的独立传输,同时通过通道选择模块对数据传输通道进行备份选择。该方案以Xilinx的Kintex 7系列FPGA为SRIO的连接设备,物理层以FPGA芯片内部集成的GTX高速串行收发器作为传输基础;传输链路采用“光模块+光缆”代替电缆实现数据的高速可靠传输,该方案已运用到遥测系统数据采集装置项目,实现了FPGA设备间双备份数据传输。