Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients, as unimodal images provide limited valid information. To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information, a new multimodality medical image fusion method(NSST-PAPCNNLatLRR) is proposed in this paper. Firstly, the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST. Then, the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients. The improved PAPCNN model was based on the automatic setting of the parameters, and the optimal method was configured for the time decay factor αe. The experimental results show that, in comparison with the five mainstream fusion algorithms, the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images, and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in six objective indexes.

A Perturbation Analysis of Low-Rank Matrix Recovery by Schatten p-Minimization

A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.

Two exact first-order k-space formulations for low-rank viscoacoustic wave propagation on staggered grids

Wave propagation in the viscoacoustic media is physically dispersive and dissipated.Completely excluding the numerical dispersion error from the physical dispersion in the viscoacoustic wave simu-lation is indispensable to understanding the intrinsic property of the wave propagation in attenuated media for the petroleum exploration geophysics.In recent years,a viscoacoustic wave equation char-acterized by fractional Laplacian gains wide attention in geophysical community.However,the first-order form of the viscoacoustic wave equation,often solved by the conventional staggered-grid pseu-dospectral method,suffers from the numerical dispersion error in time due to the low-order finite-difference approximation.It is challenging to completely eliminate the error because the viscoacoustic wave equation contains two temporal derivatives,which stem from the time stepping and the amplitude attenuation terms,respectively.To tackle the issue,we derive two exact first-order k-space viscoacoustic formulations that can fully exclude the numerical error from the physical dispersion.For the homoge-neous case,two formulations agree with the viscoacoustic analytical solution very well and have the same efficiency.For the heterogeneous case,our second k-space formulation is more efficient than the first one because the second formulation significantly reduces the number of the wavenumber-space mixed-domain operators,which are the expensive part of the viscoacoustic k-space simulation.Nu-merical cases validate that the two first-order k-space formulations are effective and efficient alternatives to the current staggered-grid pseudospectral formulation for the viscoacoustic wave simulation.

Synergistic effects of dodecane-castor oil acid mixture on the flotation responses of low-rank coal:A combined simulation and experimental study

The utilization of an appropriate collector or surfactant is crucial for the beneficiation of low-rank coal.However,in previous studies,the selection of surfactants was primarily based on flotation procedures,which hinders the understanding of the interaction mechanism between surfactant groups and oxygen-containing functional groups at the surface of low-rank coal.In this study,we investigate the flotation of low-rank coal in the presence of a composite collector by using a combined theoretical and experimental approach.The maximum flotation mass recovery achieved was 82.89%using a 3:1 mixture of dodecane and castor oil acid.Fourier-transform infrared and X-ray photoelectron spectroscopic analyses showed that castor oil acid was effectively adsorbed onto the surface of low-rank coal,enhancing the hydrophobicity of the coal.In addition,the diffusion coefficient of water molecules in the water-composite collector-coal system was greater than that in the dodecane system.Moreover,due to the presence of castor oil acid in the flotation process,the adsorption distance of dodecane and low-rank coal became shorter.Molecular dynamics simulations revealed that the diffusion and interaction of surfactant molecules at the interface of low-rank coal particles and water was enhanced because the adsorption of the dodecane-castor oil acid mixture is primarily controlled by hydrogen bonds and electrostatic attraction.Based on these results,a better surfactant for flotation of low-rank coal is also proposed.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

Research on infrared dim and small target detection algorithm based on low-rank tensor recovery

In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.

政府补助在激励企业“卡脖子”技术创新中能否提供助力——以企业参与内循环程度为调节变量

如何推动“卡脖子”技术企业创新,是近年来我国面临的重要战略问题。尝试引入锦标赛理论解释政府补助的激励机理,基于2011—2020年我国半导体及芯片行业上市企业数据,结合近年国家提出以内循环为主的新发展战略,以企业参与内循环程度为调节变量,实证研究政府补助对“卡脖子”技术企业创新产出的激励作用。结果表明,政府补助与“卡脖子”技术企业创新产出间具有倒U型关系,企业参与内循环程度在政府补助与“卡脖子”技术企业创新产出的倒U型关系中起调节作用。研究结论可为“卡脖子”技术企业在新发展格局下开展高质量创新提供参考。

突破“卡脖子”技术:知识开发模式对企业关键核心技术及其衍生技术的影响

在新一轮科技革命与百年未有之大变局下,我国企业正面临由贸易之争转变为科技实力之争带来的“卡脖子”技术困境。基于突破“卡脖子”技术的现实问题,以中国上市公司发明专利为数据集,分析企业知识开发模式对关键核心技术及其衍生技术的影响。结果表明,内部创新模式与外部创新模式均对企业关键核心技术及其衍生技术突破具有促进作用,但相比之下,外部模式产生的影响更加显著。进一步研究发现,市场竞争强度在知识开发与衍生技术之间起促进作用;选择外部知识开发模式不仅有利于衍生技术数量增长,而且有助于专利质量提高;衍生技术的成果转化会反馈新的关键核心技术突破。研究结论对我国企业高效选择创新路径,实现“卡脖子”技术突破具有重要参考价值。

以瓶颈问题为导向的本科生创新能力跨专业培养——以机械工程学科本科生毕业设计为例

本科阶段的教育是培养创新型人才的基础环节。探讨以瓶颈问题为导向的本科生创新能力跨专业协作培养方法,具体包括:促进本科生全流程积极参与实际科研项目、以瓶颈问题为导向设置本科生培养环节、跨专业协作培养以提升本科生知识融合水平,以及面向本科生的跨专业或校内资源共享等有效途径,提升本科生创新能力的培养效果,为我国创新型人才培养助力。

日间手术高质量发展瓶颈突破策略SWOT分析

目的分析日间手术运营管理创新方面的优势、劣势、机会和威胁,探索其潜在问题并制订相应的发展策略。方法2022年9月—2023年9月郑州大学第一附属医院采用SWOT分析方法,结合相关文献综述和专家意见,对日间手术高质量发展管理创新进行评估。结果日间手术可以缩短住院时间、减少床位需求,降低医疗成本、减轻患者的经济负担、提高手术效率和患者满意度。劣势在于术后需要完善的完全保障服务。机会在于有政策支持,新型手术设备和智能化管理系统的应用,社会对降低医疗费用的需求。威胁主要在于日间手术设施、人员配置和管理经验方面存在不足,另外还涉及手术风险、患者安全和法律责任等问题。结论日间手术需要借助科技创新,加强人员培训和设备更新,完善管理和运营机制,关注患者的术后护理需求,以促进管理创新和高质量发展。

面向钣金混流生产线的仿真优化研究

文章以某电梯钣金加工公司的钣金混流生产线为背景,通过虚拟仿真方法对钣金生产环节中的投产序列和瓶颈问题进行优化。首先,根据某电梯门板生产线的实际生产状况,建立生产线的仿真模型,以工件完工时间、设备空闲时间和设备总切换时间为目标,基于遗传算法,优化钣金混流生产线的投产序列;其次,通过分析缓存区添加位置和容量对钣金生产线产量的影响,解决生产线堵塞、利用率不平衡等问题。结果表明,对生产线的优化有效,经优化后的投产序列相比原始投产序列整体时间缩短约15%,通过适宜的缓存区设置,各工位设备平均利用率提高了9.6%,产量提高了14.15%。

面向带宽受限场景的高效语义通信方法

语义通信为通信系统优化和性能提升提供了新的研究角度,然而,目前语义通信的研究忽略了通信开销的影响,未考虑语义通信性能和通信开销的关系,导致带宽资源受限时语义通信性能难以提升。为此,针对带宽受限场景,提出一种基于信息瓶颈的语义通信方法。首先,该方法采用Transformer模型进行语义和信道联合编解码,并设计特征选择模块以识别和删除冗余语义信息,构建了端到端语义通信模型;进而考虑语义通信性能与通信开销之间的折衷关系,基于信息瓶颈理论设计损失函数,在保证语义通信性能的同时,降低通信开销,完成语义通信模型的训练和优化。实验结果显示,在欧洲议会平行语料库上,与基线模型相比,所提方法在保证通信性能的同时可降低约20%~30%的通信开销,在相同带宽条件下该方法的BLEU分数可提升约5%。实验结果表明,所提方法可以有效降低语义通信开销,从而提升带宽资源受限场景下的语义通信性能。

某600MW亚临界机组20%深度调峰技术方案应用探讨

新型电力系统中煤电将是最重要的调节性电源,越来越多的煤电机组将参与深度调峰。现有机组普遍面临低负荷时锅炉稳燃、受热面超温、脱硝投入、汽机安全性等问题不能实现深度调峰。对国内某600 MW亚临界机组进行了深度调峰摸底试验,得到了制约机组实现20%负荷深度调峰的瓶颈。针对瓶颈,为实现20%深度调峰,提出了锅炉侧燃烧器优化、磨煤机分离器改造、送风机改造、水平烟道吹灰优化,汽机侧辅助蒸汽系统改造,控制侧逻辑优化等技术方案。

大基金与“卡脖子”行业发展——来自半导体行业上市公司的证据

“解决‘卡脖子’难题,强化科技创新和产业链供应链韧性”是实现高质量发展需要攻克的重点任务。产业投资基金作为政府之手与市场之手相结合的政策工具,已成为扶持行业发展的重要方式。文章以2014年国家集成电路产业投资基金(大基金)成立作为准自然实验场景,以半导体行业为例,建立双重差分模型检验产业投资基金对“卡脖子”行业发展的影响。研究发现:(1)大基金显著促进半导体行业企业发展,该结论在考虑多维度混杂影响、内生性问题等一系列可能的干扰因素后依旧成立。具体表现为企业规模扩张能力、营业收入增长能力和创新能力均显著提升。(2)相比于孵化期、成熟期的企业,大基金对成长期企业的发展促进作用更加显著。此外,上述政策效应尤其体现在所处地区腐败程度较低的企业,进一步突出了“市场化”因素在产业投资基金运行中的重要性。(3)机制检验发现,大基金通过直接投资和引导投资缓解企业融资约束、通过发挥“信号效应”提振投资者信心等渠道助力企业多方位发展。文章的研究丰富了产业投资基金的效应分析及机制讨论,为实现我国科技自立自强提供可行的实施路径,也为进一步优化产业政策、突破国际困局以及保证国民经济稳定发展提供参考。