Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily d...Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.展开更多
In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory ana...In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.展开更多
Whether the multi-biological toxicity from lead exposure could be transferred to progeny has not been clarified. In the present study, we explored the Caenorhabditis elegans to analyze the multiple toxicities from lea...Whether the multi-biological toxicity from lead exposure could be transferred to progeny has not been clarified. In the present study, we explored the Caenorhabditis elegans to analyze the multiple toxicities from lead exposure and their possibly transferable properties. The lead exposure could cause series of severe multi-biological defects with a concentration-dependent manner by affecting the endpoints of life span, development, reproduction and locomotion behaviors in nematodes. Moreover, most of these toxicities could be transferred to progeny from lead exposed animals and some of the defects in progeny appeared even more severe than in their parents, such as the body sizes and mean life spans. We summarized the defects caused by lead exposure into three groups according to their transferable properties or rescue patterns. That is, the defects caused by lead exposure could be largely, or partially, or became even more severe in progeny animals. Therefore, our results suggest that lead exposure can cause severely multi-biological defects, and most of these multiple toxicities can be considered as transferable for exposed animals in C. elegans.展开更多
Based on a new idea for research on cycling of marine biogenic elements, this study showed that only the leachable form phosphorus in natural grain sizes marine sediments constitutes the transferable phosphorous in th...Based on a new idea for research on cycling of marine biogenic elements, this study showed that only the leachable form phosphorus in natural grain sizes marine sediments constitutes the transferable phosphorous in the sediments. The transferable phosphorus content in the natural grain sizes surface sediments in the Huanghe River estuary adjacent waters ranges from 58.5-69.8 μg/g, accounting for only 9.1%-11.0% of the total phosphorus content, whereas the leachable form (“transferable") phosphorus content in the sediment after it was totally ground into powder was found to be 454.8-529.2 μg/g, accounting for 73.4%-89.1% of the total phosphorus. Analysis of the correlation between the biomass of benthos and the leachable form (“transferable") phosphorus showed that most of the leachable form (“transferable") phosphorus in the totally ground sediment did not participate in the marine biogeochemical cycling. Furthermore, a synchronous survey on benthos showed that the biomass of meio and macro benthos exhibited good positive correlation with the leachable form of phosphorus in the natural grain sizes sediment, but poorer correlation with the leachable form (“transferable") phosphorus in the totally ground sediment, indicating that transferable phosphorus in marine sediment is the leachable form of phosphorus in the natural grain sizes sediments, and is not the previously known leachable form (“transferable") phosphorus obtained from the totally ground sediment.展开更多
ZnO thin films were deposited on graphite substrates by ultrasonic spray pyrolysis method with Zn（CH3COO）2·2H2O aqueous solution as precursor. The crystalline structure, morphology, and optical properties of th...ZnO thin films were deposited on graphite substrates by ultrasonic spray pyrolysis method with Zn（CH3COO）2·2H2O aqueous solution as precursor. The crystalline structure, morphology, and optical properties of the as-grown ZnO films were investigated systematically as a function of deposition temperature and growth time. Near-band edge ultraviolet （UV） emission was observed in room temperature photoluminescence spectra for the optimized samples, yet the usually observed defect related deep level emissions were nearly undetectable, indicating that high optical quality ZnO thin films could be achieved via this ultrasonic spray pyrolysis method. Considering the features of transferable and low thermal resistance of the graphite substrates, the achievement will be of special interest for the development of high-power semiconductor devices with sufficient vower durability.展开更多
In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for th...In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for the multi-agent teams to explore in the environment.Agents may achieve suboptimal policies and fail to solve some complex tasks.To improve the exploring efficiency as well as the performance of MARL tasks,in this paper,we propose a new approach by transferring the knowledge across tasks.Differently from the traditional MARL algorithms,we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of taskspecific weights.Then,we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use.Finally,once the weights for target tasks are available,it will be easier to get a well-performed policy to explore in the target domain.Hence,the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously.We evaluate the proposed algorithm on two challenging MARL tasks:cooperative boxpushing and non-monotonic predator-prey.The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.展开更多
Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to it...Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively.展开更多
In this paper, a new cash scheme is proposed for electronic payment system, in which the cash can be transferred several times. When this kind of cash is used, the fraud such as double spending can be found out but th...In this paper, a new cash scheme is proposed for electronic payment system, in which the cash can be transferred several times. When this kind of cash is used, the fraud such as double spending can be found out but the bank and the trusted party needs not be involved online in each transaction. This cash system is anonymous in normal transactions. But if a fraud happens, the trusted party can withdraw the anonymity to find out the cheater. The new cash scheme is transferable, anonymous, off-line and efficient.展开更多
With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and live...With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and lives.To identify and classify new malware variants,different types of deep learning models have been widely explored recently.Generally,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability.However,in current practical applications,an ample supply of data is absent in most specific industrial malware detection scenarios.Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem.In addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware detection.In this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks.The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.展开更多
In recent years,deep learning(DL)models have achieved signifcant progress in many domains,such as autonomous driving,facial recognition,and speech recognition.However,the vulnerability of deep learning models to adver...In recent years,deep learning(DL)models have achieved signifcant progress in many domains,such as autonomous driving,facial recognition,and speech recognition.However,the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufcient robustness and generalization.Also,transferable attacks have become a prominent method for black-box attacks.In this work,we explore the potential factors that impact adversarial examples(AEs)transferability in DL-based speech recognition.We also discuss the vulnerability of diferent DL systems and the irregular nature of decision boundaries.Our results show a remarkable diference in the transferability of AEs between speech and images,with the data relevance being low in images but opposite in speech recognition.Motivated by dropout-based ensemble approaches,we propose random gradient ensembles and dynamic gradient-weighted ensembles,and we evaluate the impact of ensembles on the transferability of AEs.The results show that the AEs created by both approaches are valid for transfer to the black box API.展开更多
This paper focuses on an important type of black-box attacks,i.e.,transfer-based adversarial attacks,where the adversary generates adversarial examples using a substitute(source)model and utilizes them to attack an un...This paper focuses on an important type of black-box attacks,i.e.,transfer-based adversarial attacks,where the adversary generates adversarial examples using a substitute(source)model and utilizes them to attack an unseen target model,without knowing its information.Existing methods tend to give unsatisfactory adversarial transferability when the source and target models are from different types of DNN architectures(e.g.,ResNet-18 and Swin Transformer).In this paper,we observe that the above phenomenon is induced by the output inconsistency problem.To alleviate this problem while effectively utilizing the existing DNN models,we propose a common knowledge learning(CKL)framework to learn better network weights to generate adversarial examples with better transferability,under fixed network architectures.Specifically,to reduce the model-specific features and obtain better output distributions,we construct a multi-teacher framework,where the knowledge is distilled from different teacher architectures into one student network.By considering that the gradient of input is usually utilized to generate adversarial examples,we impose constraints on the gradients between the student and teacher models,to further alleviate the output inconsistency problem and enhance the adversarial transferability.Extensive experiments demonstrate that our proposed work can significantly improve the adversarial transferability.展开更多
Lithography is a Key enabling technique in modern micro/nano scale technology.Achieving the optimal trade-off between resolution,throughput,and cost remains a central focus in the ongoing development.However,current l...Lithography is a Key enabling technique in modern micro/nano scale technology.Achieving the optimal trade-off between resolution,throughput,and cost remains a central focus in the ongoing development.However,current lithographic techniques such as direct-write,projection,and extreme ultraviolet lithography achieve higher resolution at the expense of increased complexity in optical systems or the use of shorter-wavelength light sources,thus raising the overall cost of production.Here,we present a cost-effective and wafer-level perfect conformal contact lithography at the diffraction limit.By leveraging a transferable photoresist,the technique ensures optimal contact between the mask and photoresist with zero-gap,facilitating the transfer of patterns at the diffraction limit while maintaining high fidelity and uniformity across large wafers.This technique applies to a wide range of complex surfaces,including non-conductive glass surfaces,flexible substrates,and curved surfaces.The proposed technique expands the potential of contact photolithography for novel device architectures and practic al manufacturing processes.展开更多
Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from anot...Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in result.The basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in prior.In other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ground-truths.However,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used directly.On the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent representations.Therefore,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain data.Unlike single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel data.Specifically,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial loss.Consequently,we can effectively learn the consistent representations for target domain considering both the structure and semantic transfer.Furthermore,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.展开更多
Bay-site carboxyl functionalized perylene diimide derivative 1,7-COOH-PDI-C_(12)(PDI-COOH)was synthesized and distinct enhanced fluorescence was observed through combining with calcium ion(Ca^(2+))in THF/H_(2)O soluti...Bay-site carboxyl functionalized perylene diimide derivative 1,7-COOH-PDI-C_(12)(PDI-COOH)was synthesized and distinct enhanced fluorescence was observed through combining with calcium ion(Ca^(2+))in THF/H_(2)O solution.The assembly and fluorescence behavior of PDI-COOH/Ca^(2+)were studied in detail by changing hydration state with different concentrations.Based on the differences in assembly morphology and stoichiometric ratios of PDICOOH/Ca^(2+),we proposed the fluorescence emission mechanism of PDI-COOH/Ca^(2+)in THF/H_(2)O and THF,respectively.This work reveals a novel strategy of aggregated state fluorescence enhancement and reminds us of the important role of water in molecular fluorescence emission and assembly.展开更多
In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferab...In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferable entropy notion are put. The character is studied. An potential by the relation entropy and transferable entropy are defined. It is the consistency measure on the group between a single decision making. We gained a new aggregation effective definition on the group misjudge.展开更多
Full-color imaging is essential in digital pathology for accurate tissue analysis.Utilizing advanced optical modulation and phase retrieval algorithms,Fourier ptychographic microscopy(FPM)offers a powerful solution fo...Full-color imaging is essential in digital pathology for accurate tissue analysis.Utilizing advanced optical modulation and phase retrieval algorithms,Fourier ptychographic microscopy(FPM)offers a powerful solution for high-throughput digital pathology,combining high resolution,large field of view,and extended depth of field(DOF).However,the full-color capabilities of FPM are hindered by coherent color artifacts and reduced computational efficiency,which significantly limits its practical applications.Color-transferbased FPM(CFPM)has emerged as a potential solution,theoretically reducing both acquisition and reconstruction threefold time.Yet,existing methods fall short of achieving the desired reconstruction speed and colorization quality.In this study,we report a generalized dual-color-space constrained model for FPM colorization.This model provides a mathematical framework for model-based FPM colorization,enabling a closed-form solution without the need for redundant iterative calculations.Our approach,termed generalized CFPM(gCFPM),achieves colorization within seconds for megapixel-scale images,delivering superior colorization quality in terms of both colorfulness and sharpness,along with an extended DOF.Both simulations and experiments demonstrate that gCFPM surpasses state-of-the-art methods across all evaluated criteria.Our work offers a robust and comprehensive workflow for high-throughput full-color pathological imaging using FPM platforms,laying a solid foundation for future advancements in methodology and engineering.展开更多
Objective Poxviruses are zoonotic pathogens that infect humans,mammals,vertebrates,and arthropods.However,the specific role of ticks in transmission and evolution of these viruses remains unclear.Methods Transcriptomi...Objective Poxviruses are zoonotic pathogens that infect humans,mammals,vertebrates,and arthropods.However,the specific role of ticks in transmission and evolution of these viruses remains unclear.Methods Transcriptomic and metatranscriptomic raw data from 329 sampling pools of seven tick species across five continents were mined to assess the diversity and abundance of poxviruses.Chordopoxviral sequences were assembled and subjected to phylogenetic analysis to trace the origins of the unblasted fragments within these sequences.Results Fifty-eight poxvirus species,representing two subfamilies and 20 genera,were identified,with 212 poxviral sequences assembled.A substantial proportion of AT-rich fragments were detected in the assembled poxviral genomes.These genomic sequences contained fragments originating from rodents,archaea,and arthropods.Conclusion Our findings indicate that ticks play a significant role in the transmission and evolution of poxviruses.These viruses demonstrate the capacity to modulate virulence and adaptability through horizontal gene transfer,gene recombination,and gene mutations,thereby promoting co-existence and co-evolution with their hosts.This study advances understanding of the ecological dynamics of poxvirus transmission and evolution and highlights the potential role of ticks as vectors and vessels in these processes.展开更多
This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs...This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcom...BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC.However,the performance of radiomic and deep transfer learning(DTL)models derived from biparametric magnetic resonance imaging(bpMRI)in predicting Ki-67 risk stratification and recurrence-free survival(RFS)in patients with HCC remains limited.AIM To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.METHODS This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI.Ki-67 risk stratification was categorized as high(>20%)or low(≤20%)according to immunohistochemical staining.Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models,respectively.Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification,and a predictive nomogram model was developed.RESULTS A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification.The area under the curve(AUC)of the clinical model was 0.77,while those of the radiomic and DTL models were 0.81 and 0.87,respectively,for the prediction of high Ki-67 risk stratification,and the nomogram model achieved a better AUC of 0.92.The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months,respectively(P<0.001).Additionally,patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification(33.53 vs 66.74 months,P=0.007).CONCLUSION Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
基金supported in part by the National Natural Science Foundation(62202118,U24A20241)in part by Major Scientific and Technological Special Project of Guizhou Province([2024]014,[2024]003)+1 种基金in part by Scientific and Technological Research Projects from Guizhou Education Department(Qian jiao ji[2023]003)in part by Guizhou Science and Technology Department Hundred Level Innovative Talents Project(GCC[2023]018).
文摘Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.
基金supported by the National Natural Science Foundation of China(62333010,61673205).
文摘In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.
基金Project supported by the Southeast University Foundation for Excellent Young Scholars(No.4023001013)
文摘Whether the multi-biological toxicity from lead exposure could be transferred to progeny has not been clarified. In the present study, we explored the Caenorhabditis elegans to analyze the multiple toxicities from lead exposure and their possibly transferable properties. The lead exposure could cause series of severe multi-biological defects with a concentration-dependent manner by affecting the endpoints of life span, development, reproduction and locomotion behaviors in nematodes. Moreover, most of these toxicities could be transferred to progeny from lead exposed animals and some of the defects in progeny appeared even more severe than in their parents, such as the body sizes and mean life spans. We summarized the defects caused by lead exposure into three groups according to their transferable properties or rescue patterns. That is, the defects caused by lead exposure could be largely, or partially, or became even more severe in progeny animals. Therefore, our results suggest that lead exposure can cause severely multi-biological defects, and most of these multiple toxicities can be considered as transferable for exposed animals in C. elegans.
基金Project 49776300 supported by NSFC49925614 by the NSFC for Outstanding Young Scientists.
文摘Based on a new idea for research on cycling of marine biogenic elements, this study showed that only the leachable form phosphorus in natural grain sizes marine sediments constitutes the transferable phosphorous in the sediments. The transferable phosphorus content in the natural grain sizes surface sediments in the Huanghe River estuary adjacent waters ranges from 58.5-69.8 μg/g, accounting for only 9.1%-11.0% of the total phosphorus content, whereas the leachable form (“transferable") phosphorus content in the sediment after it was totally ground into powder was found to be 454.8-529.2 μg/g, accounting for 73.4%-89.1% of the total phosphorus. Analysis of the correlation between the biomass of benthos and the leachable form (“transferable") phosphorus showed that most of the leachable form (“transferable") phosphorus in the totally ground sediment did not participate in the marine biogeochemical cycling. Furthermore, a synchronous survey on benthos showed that the biomass of meio and macro benthos exhibited good positive correlation with the leachable form of phosphorus in the natural grain sizes sediment, but poorer correlation with the leachable form (“transferable") phosphorus in the totally ground sediment, indicating that transferable phosphorus in marine sediment is the leachable form of phosphorus in the natural grain sizes sediments, and is not the previously known leachable form (“transferable") phosphorus obtained from the totally ground sediment.
基金Funded by the Fundamental Research Funds for the Central Universities(No.DUT12ZD(G)01)the Opening Project of Key Laboratory of Inorganic Coating Materials,Chinese Academy of Sciences(No.KLICM-2012-01)
文摘ZnO thin films were deposited on graphite substrates by ultrasonic spray pyrolysis method with Zn（CH3COO）2·2H2O aqueous solution as precursor. The crystalline structure, morphology, and optical properties of the as-grown ZnO films were investigated systematically as a function of deposition temperature and growth time. Near-band edge ultraviolet （UV） emission was observed in room temperature photoluminescence spectra for the optimized samples, yet the usually observed defect related deep level emissions were nearly undetectable, indicating that high optical quality ZnO thin films could be achieved via this ultrasonic spray pyrolysis method. Considering the features of transferable and low thermal resistance of the graphite substrates, the achievement will be of special interest for the development of high-power semiconductor devices with sufficient vower durability.
基金the National Key R&D Program of China(2021ZD0112700,2018AAA0101400)the National Natural Science Foundation of China(62173251,61921004,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20202006)。
文摘In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for the multi-agent teams to explore in the environment.Agents may achieve suboptimal policies and fail to solve some complex tasks.To improve the exploring efficiency as well as the performance of MARL tasks,in this paper,we propose a new approach by transferring the knowledge across tasks.Differently from the traditional MARL algorithms,we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of taskspecific weights.Then,we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use.Finally,once the weights for target tasks are available,it will be easier to get a well-performed policy to explore in the target domain.Hence,the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously.We evaluate the proposed algorithm on two challenging MARL tasks:cooperative boxpushing and non-monotonic predator-prey.The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.
基金This work was supported by the National Natural Science Foundation of China(52275130)the National Key Research and Development Program of China(2018YFB1702400).
文摘Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively.
基金Supported by the Natural Science Foundation of Zhejiang Province, China (Y106802)Guangdong Provincial Science and Technology Planning Project of China (2007B010200035)
文摘In this paper, a new cash scheme is proposed for electronic payment system, in which the cash can be transferred several times. When this kind of cash is used, the fraud such as double spending can be found out but the bank and the trusted party needs not be involved online in each transaction. This cash system is anonymous in normal transactions. But if a fraud happens, the trusted party can withdraw the anonymity to find out the cheater. The new cash scheme is transferable, anonymous, off-line and efficient.
基金the National Natural Science Foundation of China under Grants U1836106 and 81961138010the Beijing Natural Science Foundation under Grants 19L2029 and M21032+2 种基金the Scientific and Technological Innovation Foundation of Foshan under Grants BK20BF010 and BK21BF001the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB,under Grant BK19BF006by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.
文摘With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and lives.To identify and classify new malware variants,different types of deep learning models have been widely explored recently.Generally,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability.However,in current practical applications,an ample supply of data is absent in most specific industrial malware detection scenarios.Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem.In addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware detection.In this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks.The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.
基金supported in part by NSFC No.62202275 and Shandong-SF No.ZR2022QF012 projects.
文摘In recent years,deep learning(DL)models have achieved signifcant progress in many domains,such as autonomous driving,facial recognition,and speech recognition.However,the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufcient robustness and generalization.Also,transferable attacks have become a prominent method for black-box attacks.In this work,we explore the potential factors that impact adversarial examples(AEs)transferability in DL-based speech recognition.We also discuss the vulnerability of diferent DL systems and the irregular nature of decision boundaries.Our results show a remarkable diference in the transferability of AEs between speech and images,with the data relevance being low in images but opposite in speech recognition.Motivated by dropout-based ensemble approaches,we propose random gradient ensembles and dynamic gradient-weighted ensembles,and we evaluate the impact of ensembles on the transferability of AEs.The results show that the AEs created by both approaches are valid for transfer to the black box API.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62272020 and U20B2069)in part by the State Key Laboratory of Complex&Critical Software Environment(SKLSDE2023ZX-16)in part by the Fundamental Research Funds for Central Universities.
文摘This paper focuses on an important type of black-box attacks,i.e.,transfer-based adversarial attacks,where the adversary generates adversarial examples using a substitute(source)model and utilizes them to attack an unseen target model,without knowing its information.Existing methods tend to give unsatisfactory adversarial transferability when the source and target models are from different types of DNN architectures(e.g.,ResNet-18 and Swin Transformer).In this paper,we observe that the above phenomenon is induced by the output inconsistency problem.To alleviate this problem while effectively utilizing the existing DNN models,we propose a common knowledge learning(CKL)framework to learn better network weights to generate adversarial examples with better transferability,under fixed network architectures.Specifically,to reduce the model-specific features and obtain better output distributions,we construct a multi-teacher framework,where the knowledge is distilled from different teacher architectures into one student network.By considering that the gradient of input is usually utilized to generate adversarial examples,we impose constraints on the gradients between the student and teacher models,to further alleviate the output inconsistency problem and enhance the adversarial transferability.Extensive experiments demonstrate that our proposed work can significantly improve the adversarial transferability.
基金supported by the National Key Research and Development Program of China (2022YFB4602600)National Natural Science Foundation of China (Grant Nos. 52425508 & 52221001)the Hunan Provincial Natural Science Foundation of China (2025JJ60286)。
文摘Lithography is a Key enabling technique in modern micro/nano scale technology.Achieving the optimal trade-off between resolution,throughput,and cost remains a central focus in the ongoing development.However,current lithographic techniques such as direct-write,projection,and extreme ultraviolet lithography achieve higher resolution at the expense of increased complexity in optical systems or the use of shorter-wavelength light sources,thus raising the overall cost of production.Here,we present a cost-effective and wafer-level perfect conformal contact lithography at the diffraction limit.By leveraging a transferable photoresist,the technique ensures optimal contact between the mask and photoresist with zero-gap,facilitating the transfer of patterns at the diffraction limit while maintaining high fidelity and uniformity across large wafers.This technique applies to a wide range of complex surfaces,including non-conductive glass surfaces,flexible substrates,and curved surfaces.The proposed technique expands the potential of contact photolithography for novel device architectures and practic al manufacturing processes.
基金supported by the National Key R&D Program of China(2022YFF0712100)the National Natural Science Foundation of China(Grant Nos.62006118,62276131,62006119)+2 种基金Natural Science Foundation of Jiangsu Province of China(BK20200460)Jiangsu Shuangchuang(Mass Innovation and Entrepreneurship)Talent ProgramYoung Elite Scientists Sponsorship Program by CAST,the Fundamental Research Funds for the Central Universities(Nos.NJ2022028,30922010317).
文摘Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in result.The basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in prior.In other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ground-truths.However,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used directly.On the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent representations.Therefore,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain data.Unlike single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel data.Specifically,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial loss.Consequently,we can effectively learn the consistent representations for target domain considering both the structure and semantic transfer.Furthermore,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.
文摘Bay-site carboxyl functionalized perylene diimide derivative 1,7-COOH-PDI-C_(12)(PDI-COOH)was synthesized and distinct enhanced fluorescence was observed through combining with calcium ion(Ca^(2+))in THF/H_(2)O solution.The assembly and fluorescence behavior of PDI-COOH/Ca^(2+)were studied in detail by changing hydration state with different concentrations.Based on the differences in assembly morphology and stoichiometric ratios of PDICOOH/Ca^(2+),we proposed the fluorescence emission mechanism of PDI-COOH/Ca^(2+)in THF/H_(2)O and THF,respectively.This work reveals a novel strategy of aggregated state fluorescence enhancement and reminds us of the important role of water in molecular fluorescence emission and assembly.
基金This projectis supported by National Natural Science Foundation of China( No.79930 90 0 )
文摘In this paper, aggregation question based on group decision making and a single decision making is studied. The theory of entropy is applied to the sets pair analysis. The system of relation entropy and the transferable entropy notion are put. The character is studied. An potential by the relation entropy and transferable entropy are defined. It is the consistency measure on the group between a single decision making. We gained a new aggregation effective definition on the group misjudge.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104500 and 82430062)the Key Research and Development Projects of Shaanxi Province(Grant No.2023-YBSF-263),the Shenzhen Engineering Research Centre(Grant No.XMHT20230115004)the Shenzhen Science and Technology Innovation Commission(Grant No.KCXFZ20201221173207022).
文摘Full-color imaging is essential in digital pathology for accurate tissue analysis.Utilizing advanced optical modulation and phase retrieval algorithms,Fourier ptychographic microscopy(FPM)offers a powerful solution for high-throughput digital pathology,combining high resolution,large field of view,and extended depth of field(DOF).However,the full-color capabilities of FPM are hindered by coherent color artifacts and reduced computational efficiency,which significantly limits its practical applications.Color-transferbased FPM(CFPM)has emerged as a potential solution,theoretically reducing both acquisition and reconstruction threefold time.Yet,existing methods fall short of achieving the desired reconstruction speed and colorization quality.In this study,we report a generalized dual-color-space constrained model for FPM colorization.This model provides a mathematical framework for model-based FPM colorization,enabling a closed-form solution without the need for redundant iterative calculations.Our approach,termed generalized CFPM(gCFPM),achieves colorization within seconds for megapixel-scale images,delivering superior colorization quality in terms of both colorfulness and sharpness,along with an extended DOF.Both simulations and experiments demonstrate that gCFPM surpasses state-of-the-art methods across all evaluated criteria.Our work offers a robust and comprehensive workflow for high-throughput full-color pathological imaging using FPM platforms,laying a solid foundation for future advancements in methodology and engineering.
基金financially supported by the Shanghai New Three-Year Action Plan for Public Health(Grant No.GWVI-11.1-03)National Natural Science Foundation of China(Grant No.81872673).
文摘Objective Poxviruses are zoonotic pathogens that infect humans,mammals,vertebrates,and arthropods.However,the specific role of ticks in transmission and evolution of these viruses remains unclear.Methods Transcriptomic and metatranscriptomic raw data from 329 sampling pools of seven tick species across five continents were mined to assess the diversity and abundance of poxviruses.Chordopoxviral sequences were assembled and subjected to phylogenetic analysis to trace the origins of the unblasted fragments within these sequences.Results Fifty-eight poxvirus species,representing two subfamilies and 20 genera,were identified,with 212 poxviral sequences assembled.A substantial proportion of AT-rich fragments were detected in the assembled poxviral genomes.These genomic sequences contained fragments originating from rodents,archaea,and arthropods.Conclusion Our findings indicate that ticks play a significant role in the transmission and evolution of poxviruses.These viruses demonstrate the capacity to modulate virulence and adaptability through horizontal gene transfer,gene recombination,and gene mutations,thereby promoting co-existence and co-evolution with their hosts.This study advances understanding of the ecological dynamics of poxvirus transmission and evolution and highlights the potential role of ticks as vectors and vessels in these processes.
文摘This article presents a detailed theoretical hybrid analysis of the magnetism and the thermal radiative heat transfer in the presence of heat generation affecting the behavior of the dispersed gold nanoparticles(AuNPs)through the blood vessels of the human body.The rheology of gold-blood nanofluid is treated as magnetohydrodynamic(MHD)flow with ferromagnetic properties.The AuNPs take different shapes as bricks,cylinders,and platelets which are considered in changing the nanofluid flow behavior.Physiologically,the blood is circulated under the kinetics of the peristaltic action.The mixed properties of the slip flow,the gravity,the space porosity,the transverse ferromagnetic field,the thermal radiation,the nanoparticles shape factors,the peristaltic amplitude ratio,and the concentration of the AuNPs are interacted and analyzed for the gold-blood circulation in the inclined tube.The appropriate model for the thermal conductivity of the nanofluid is chosen to be the effective Hamilton-Crosser model.The undertaken nanofluid can be treated as incompressible non-Newtonian ferromagnetic fluid.The solutions of the partial differential governing equations of the MHD nanofluid flow are executed by the strategy of perturbation approach under the assumption of long wavelength and low Reynolds number.Graphs for the streamwise velocity distributions,temperature distributions,pressure gradients,pressure drops,and streamlines are presented under the influences of the pertinent properties.The practical implementation of this research finds application in treating cancer through a technique known as photothermal therapy(PTT).The results indicate the control role of the magnetism,the heat generation,the shape factors of the AuNPs,and its concentration on the enhancement of the thermal properties and the streamwise velocity of the nanofluid.The results reveal a marked enhancement in the temperature profiles of the nanofluid,prominently influenced by both the intensified heat source and the heightened volume fractions of the nanoparticles.Furthermore,the platelet shape is regarded as most advantageous for heat conduction owing to its highest effective thermal conductivity.AuNPs proved strong efficiency in delivering and targeting the drug to reach the affected area with tumors.These results offer valuable insights into evaluating the effectiveness of PTT in addressing diverse cancer conditions and regulating their progression.
基金Supported by Clinical Trials from the Third Affiliated Hospital of Soochow University,No.2024-156Changzhou Science and Technology Program,No.CJ20244017。
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent and life-threatening cancer with increasing incidence worldwide.High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC.However,the performance of radiomic and deep transfer learning(DTL)models derived from biparametric magnetic resonance imaging(bpMRI)in predicting Ki-67 risk stratification and recurrence-free survival(RFS)in patients with HCC remains limited.AIM To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.METHODS This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI.Ki-67 risk stratification was categorized as high(>20%)or low(≤20%)according to immunohistochemical staining.Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models,respectively.Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification,and a predictive nomogram model was developed.RESULTS A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification.The area under the curve(AUC)of the clinical model was 0.77,while those of the radiomic and DTL models were 0.81 and 0.87,respectively,for the prediction of high Ki-67 risk stratification,and the nomogram model achieved a better AUC of 0.92.The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months,respectively(P<0.001).Additionally,patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification(33.53 vs 66.74 months,P=0.007).CONCLUSION Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
