Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engine...Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.展开更多
Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they re...Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.展开更多
Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry.Owing to economic and security concerns,a common method is to train data generated by simulators.However,achieving a sat...Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry.Owing to economic and security concerns,a common method is to train data generated by simulators.However,achieving a satisfactory performance in practical applications is difficult because simulators imperfectly emulate reality.To bridge this gap,we propose a novel framework called simulation-to-reality domain adaptation(SRDA)for forecasting the operating parameters of nuclear reactors.The SRDA model employs a transformer-based feature extractor to capture dynamic characteristics and temporal dependencies.A parameter predictor with an improved logarithmic loss function is specifically designed to adapt to varying reactor powers.To fuse prior reactor knowledge from simulations with reality,the domain discriminator utilizes an adversarial strategy to ensure the learning of deep domain-invariant features,and the multiple kernel maximum mean discrepancy minimizes their discrepancies.Experiments on neutron fluxes and temperatures from a pressurized water reactor illustrate that the SRDA model surpasses various advanced methods in terms of predictive performance.This study is the first to use domain adaptation for real-world reactor prediction and presents a feasible solution for enhancing the transferability and generalizability of simulated data.展开更多
[Objective] The research aimed to study the transferability of Camellia sinensis EST-SSRs in Theaceae plant.[Method] Seven pairs of EST-SSRs primers which derived from Camellia sinensis EST sequence were used to ampli...[Objective] The research aimed to study the transferability of Camellia sinensis EST-SSRs in Theaceae plant.[Method] Seven pairs of EST-SSRs primers which derived from Camellia sinensis EST sequence were used to amplify the nineteen materials of Theaceae plant.[Result] Five pairs in the seven pairs of EST-SSRs primers could effectively amplify the nineteen tested varieties,and the transferability rate was 71.43%.The amplification rate of Camellia retiacalate FengShanCha,Camellia japouica CaiXia and Camellia retiacalate JuBan was the highest.The amplification rate of Camellia synaptica Sealy and Adinandra sagonica var.wallichiana（oc）Ming was the lowest.Moreover,four pairs in the five pairs of primers which could effectively amplify showed the rich polymorphism whose difference was obvious in the tested materials.[Conclusion] SSR primers which were developed from Camellia sinensis genome had the higher transferability in the different genus and species of Theaceae plant,could be used in the comparative genome research and analysis mark research of Theaceae plant.展开更多
Simple sequence repeat （SSR） or microsatellite marker is a valuable tool for several purposes, such as mapping, fingerprinting, and breeding. In the present study, an intersimple sequence repeat （ISSR）-PCR techniq...Simple sequence repeat （SSR） or microsatellite marker is a valuable tool for several purposes, such as mapping, fingerprinting, and breeding. In the present study, an intersimple sequence repeat （ISSR）-PCR technique was applied for developing SSR markers in non-heading Chinese cabbage （Brassica rapa）. A total of 190 SSRs were obtained. Among these, AG or CT （54.7%） was the most frequent repeat, followed by AC or GT （31.6%） of the microsatellites. The average number of the SSRs length array was 16 and 10 times, respectively. Based on the determined SSR sequences, 143 SSR primer pairs were designed to evaluate their transferabilities among the related species of Brassica. The number of alleles produced per marker averaged 2.91, and the polymorphism information content （PIC） value ranged from 0 to 0.863 with an average of 0.540. Monomorphism was observed in 16 primer pairs. The transferability percentage in CC genome was higher than in BB genome. More loci occurred in the BBCC genome. This result supported the hypothesis that BB genome was divergent from A and C genomes, and AA and CC genomes were relatively close. The polymorphic primers can be exploited for further evolution, fingerprinting, and variety identification.展开更多
Currently development of new marker types has shifted from anonymous DNA fragments to gene-based markers. Simple Sequence Repeats (SSRs) are useful DNA markers in plant genetic research including in peanut. However, d...Currently development of new marker types has shifted from anonymous DNA fragments to gene-based markers. Simple Sequence Repeats (SSRs) are useful DNA markers in plant genetic research including in peanut. However, de novo development of SSRs is expensive and time consuming. Gene-based DNA markers are transferable among related species owing to the conserved nature of genes. In this study transferability of sorghum EST-SSR (SbEST-SSR) markers to peanut was prospected. A set of 411 SbEST-SSR primer pairs were used to amplify peanut genomic DNA extracted from cultivated peanut where 39% of them successfully amplified. A comparison of amplification patterns between sorghum and peanut showed similar banding pattern with majority of transferable SbEST-SSRs. Among these transferable SSR markers, 14% have detected polymorphism among 4 resistant and 4 susceptible peanut lines for rust and late leaf spot diseases. These transferable markers will benefit peanut genome research by not only providing additional DNA markers for population genetic analyses, but also allowing comparative mapping to be possible between peanut and sorghum—a possible monocot-dicot comparison.展开更多
We characterized 14 anonymous nuclear loci from Pinus thunbergii Parl., an important pine species native to Japan. One hundred and twenty-six single nucleotide polymorphisms （SNPs） were identified from these loci, g...We characterized 14 anonymous nuclear loci from Pinus thunbergii Parl., an important pine species native to Japan. One hundred and twenty-six single nucleotide polymorphisms （SNPs） were identified from these loci, giving a frequency of 1 SNP per 51 bp. Nucleotide di- versity （0） ranged from 1.06 × 10^-3 to 11.87 × 10^-3, with all average of 4.99 × 10^-3. Only one locus （mK45） deviated significantly from the Hardy-Weinberg equilibrium. Thirteen of 14 loci were applicable in other pine species. These loci will be useful for nucleotide variation studies and will provide material for SNP-based marker development in P. thun- bergii and related species.展开更多
Glacier mass balance, the difference between accumulation and ablation at the glacier surface, is the direct reflection of the local climate regime. Under global warming, the simulation of glacier mass balance at the ...Glacier mass balance, the difference between accumulation and ablation at the glacier surface, is the direct reflection of the local climate regime. Under global warming, the simulation of glacier mass balance at the regional scale has attracted increasing interests. This study selects Urumqi Glacier No. 1 as the testbed for examining the transferability in space and time of two commonly used glacier mass balance simulation models: i.e., the Degree-Day Model(DDM) and the simplified Energy Balance Model(s EBM). Four experiments were carried out for assessing both models’ temporal and spatial transferability. The results show that the spatial transferability of both the DDM and s EBM is strong, whereas the temporal transferability of the DDM is relatively weak. For all four experiments, the overall simulation effect of the s EBM is better than that of the DDM. At the zone around Equilibrium Line Altitude(ELA), the DDM performed better than the s EBM.Also, the accuracy of parameters, including the lapse rate of air temperature and vertical gradient of precipitation at the glacier surface, is of great significance for improving the spatial transferability of both models.展开更多
The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario.However,they often exhibit weak transferability in the black-box scenario,especially when attacki...The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario.However,they often exhibit weak transferability in the black-box scenario,especially when attacking those with defense mechanisms.In this work,we propose a new transfer-based blackbox attack called the channel decomposition attack method(CDAM).It can attack multiple black-box models by enhancing the transferability of the adversarial examples.On the one hand,it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate gradient.On the other hand,it helps to escape from local optima by initializing the data point with random noise.Besides,it could combine with other transfer-based attacks flexibly.Extensive experiments on the standard ImageNet dataset show that our method could significantly improve the transferability of adversarial attacks.Compared with the state-of-the-art method,our approach improves the average success rate from 88.2%to 96.6%when attacking three adversarially trained black-box models,demonstrating the remaining shortcomings of existing deep learning models.展开更多
Transferability of five nuclear microsatellite markers (Jc-16, Jc-31, Jc-32, Jc-35 and Jc-37) that were originally developed for J. communis was tested to J. procera. Jc-31 & Jc-37 showed successful amplifications...Transferability of five nuclear microsatellite markers (Jc-16, Jc-31, Jc-32, Jc-35 and Jc-37) that were originally developed for J. communis was tested to J. procera. Jc-31 & Jc-37 showed successful amplifications and polymorphism in J. procera. Jc-35 which had been reported as polymorphic in J. communis was monomorphic in J. procera while the primer pair for Jc-32 failed to record any amplification. The remaining one primer pair (Jc-16) showed double loci ampli-fication in both J. procera and the control J. communis suggesting further examination of the primer pair and its binding sites. Genetic variation of six Ethiopian J. procera populations: Chilimo, Goba, Menagesha-Suba, Wef-Washa, Yabelo and Ziquala was assessed based on the two polymorphic loci (Jc-31 & Jc-37) in 20 - 24 individuals of each population. From these two loci, a total of 41 alleles could be retrieved. Two populations that are located south east of the Great Rift Valley together harboured 75% of private alleles signifying their deviant geo-ecological zones and suggesting special consideration for conservation. Chilimo, which is at the western margin of Juniper habitat in Ethiopian central highlands scored the highest fixation (FIS = 0.584) entailing lower immigrant genes and hence higher inbreeding. The AMOVA revealed that 97% of the variation resided within the?population while still among population variation was significant展开更多
Safety performance functions(SPFs),or crash-prediction models,have played an important role in identifying the factors contributing to crashes,predicting crash counts and identifying hotspots.Since a great deal of tim...Safety performance functions(SPFs),or crash-prediction models,have played an important role in identifying the factors contributing to crashes,predicting crash counts and identifying hotspots.Since a great deal of time and effort is needed to estimate an SPF,previous studies have sought to determine the transferability of particular SPFs;that is,the extent to which they can be applied to data from other regions.Although many efforts have been made to examine micro-level SPF transferability,few studies have focused on macro-level SPF transferability.There has been little transferability analysis of macro-level SPFs in the international context,especially between western countries.This study therefore evaluates the transferability of SPFs for several states in the USA(Illinois,Florida and Colorado)and for Italy.The SPFs were developed using data from counties in the United States and provincias in Italy,and the results revealed multiple common significant variables between the two countries.Transferability indexes were then calculated between the SPFs.These showed that the Italy SPFs for total crashes and bicycle crashes were transferable to US data after calibration factors were applied,whereas the US SPFs for total and bicycle crashes,with the exception of the Colorado SPF,could not be transferred to the Italian data.On the other hand,none of the pedestrian SPFs developed was transferable to other countries.This paper provides insights into the applicability of macro-level SPFs between the USA and Italy,and shows a good potential for international SPF transferability.Nevertheless,further investigation is needed of SPF transferability between a wider range of countries.展开更多
It is of great significance to study the indicators of university patents’transferability for improving the efficiency of the University Technology Transfer Office and promoting university patent transfer.Based on th...It is of great significance to study the indicators of university patents’transferability for improving the efficiency of the University Technology Transfer Office and promoting university patent transfer.Based on the in-depth analysis of the existing research,this paper finds that patent quality is the inherent decisive factor of patent transferability.Combining with the evaluation indexes of patent quality and the bibliometrics characteristics of university patents,9 indicators are proposed to indicate the transferability of university patents.Based on the patent transfer data of 35 Chinese universities,this paper analyzes and verifies the potential indicators of patent transfer using the binary logistic regression method.The results show that the number of inventors and the number of non-patent document citations positively predict the transferability of university patents,while the examination duration negatively predicts transferability.The effects of other indicators on transferability need to be discussed considering the actual situation and specific technology fields.展开更多
This study examined the influence of spatial resolution on model parameterization,output,and the parameter transferability between different resolutions using the Storm Water Management Model.High-resolution models,in...This study examined the influence of spatial resolution on model parameterization,output,and the parameter transferability between different resolutions using the Storm Water Management Model.High-resolution models,in which most subcatchrnents were homogeneous,and high-resolution-based low-resolution models (in 3 scenarios)were constructed for a highly urbanized catchment in Beijing.The results indicated that the parameterization and simulation results were affected by both spatial resolution and rainfall characteristics.The simulated peak inflow and total runoff volume were sensitive to the spatial resolution,but did not show a consistent tendency.High-resolution models performed very well for both calibration and validation events in terms of three indexes:1)the Nash-Sutcliffe efficiency, 2)the peak flow error,and 3)the volume error;indication of the advantage of using these models.The parameters obtained from high-resolution models could be directly used in the low-resolution models and performed well in the simulation of heavy rain and torrential rain and in the study area where sub-area routing is insignificant.Alternatively,sub-area routing should be considered and estimated approximately.The successful scale conversion from high spatial resolution to low spatial resolution is of great significance for the hydrological simulation of ungauged large areas.展开更多
Adding subtle perturbations to an image can cause the classification model to misclassify,and such images are called adversarial examples.Adversar-ial examples threaten the safe use of deep neural networks,but when com...Adding subtle perturbations to an image can cause the classification model to misclassify,and such images are called adversarial examples.Adversar-ial examples threaten the safe use of deep neural networks,but when combined with reversible data hiding(RDH)technology,they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models.Based on this,the reversible adversarial example(RAE)is ris-ing.However,existing RAE technology focuses on feasibility,attack success rate and image quality,but ignores transferability and time complexity.In this paper,we optimize the data hiding structure and combine data augmentation technology,whichflips the input image in probability to avoid overfitting phenomenon on the dataset.On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality,the proposed method improves the transferability of reversible adversarial examples by approximately 16%and reduces the com-putational cost by approximately 43%compared to the state-of-the-art method.In addition,the appropriateflip probability can be selected for different application scenarios.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies.Particularly useful for travel analysis is when...Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies.Particularly useful for travel analysis is when Social Media users allow(voluntarily or not)tracing their movement using geotagged information of their communication with these online platforms.In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns,study differences and commonalities among these cities,and explore the nature of user location recurrence.The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models.Identification of repeated rates of location(recurrence)can be used to define activity spaces.Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here.The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.展开更多
Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique ...Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique advantages of charge delocalization and enrich uncoordinated electrons and short-range transfer kinetics,which are crucial to achieve rapid low-temperature charge transfer and high-temperature interface stability.Herein,a quantum-scale FeS_(2) loaded on three-dimensional Ti_(3)C_(2) MXene skeletons(FeS_(2) QD/MXene)fabricated as SIBs anode,demonstrating impressive performance under wide-temperature conditions(−35 to 65).The theoretical calculations combined with experimental characterization interprets that the unsaturated coordination edges of FeS_(2) QD can induce delocalized electronic regions,which reduces electrostatic potential and significantly facilitates efficient Na+diffusion across a broad temperature range.Moreover,the Ti_(3)C_(2) skeleton reinforces structural integrity via Fe-O-Ti bonding,while enabling excellent dispersion of FeS_(2) QD.As expected,FeS_(2) QD/MXene anode harvests capacities of 255.2 and 424.9 mAh g^(−1) at 0.1 A g^(−1) under−35 and 65,and the energy density of FeS_(2) QD/MXene//NVP full cell can reach to 162.4 Wh kg^(−1) at−35,highlighting its practical potential for wide-temperatures conditions.This work extends the uncoordinated regions induced by quantum-size effects for exceptional Na^(+)ion storage and diffusion performance at wide-temperatures environment.展开更多
Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been...Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
Advances in machine learning(ML)have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost,parallel efficiency of empirical potentials.However,ML-base...Advances in machine learning(ML)have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost,parallel efficiency of empirical potentials.However,ML-based potentials struggle to achieve transferability,i.e.,provide consistent accuracy across configurations that differ from those used during training.In order to realize the promise of ML-based potentials,systematic and scalable approaches to generate diverse training sets need to be developed.This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach.Subsequently,multiple polynomial and neural network potentials are trained on the entropy-optimized dataset.A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison.The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models.Furthermore,the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.展开更多
基金supported by the Research Grants Council of Hong Kong(CityU 11305919 and 11308620)and NSFC/RGC Joint Research Scheme N_CityU104/19Hong Kong Research Grant Council Collaborative Research Fund:C1002-21G and C1017-22Gsupported by the Hong Kong Research Grant Council Collaborative Research Fund:C6021-19E.
文摘Developing machine learning frameworks with predictive power,interpretability,and transferability is crucial,yet it faces challenges in the field of electrocatalysis.To achieve this,we employed rigorous feature engineering to establish a finely tuned gradient boosting regressor(GBR)model,which adeptly captures the physical complexity from feature space to target variables.We demonstrated that environmental electron effects and atomic number significantly govern the success of the mapping process via global and local explanations.The finely tuned GBR model exhibits exceptional robustness in predicting CO adsorption energies(R_(ave)^(2)=0.937,RMSE=0.153 eV).Moreover,the model demonstrated remarkable transfer learning ability,showing excellent predictive power for OH,NO,and N_(2) adsorption.Importantly,the GBR model exhibits exceptional predictive capability across an extensive search space,thereby demonstrating profound adaptability and versatility.Our research framework significantly enhances the interpretability and transferability of machine learning in electrocatalysis,offering vital insights for further advancements.
基金supported by the Intelligent Policing Key Laboratory of Sichuan Province(No.ZNJW2022KFZD002)This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202302403,KJQN202303111).
文摘Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.
基金supported by the Industry-University Cooperation Project in Fujian Province University(No.2023H6006)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment(No.EERI-KF20200005)。
文摘Artificial intelligence has potential for forecasting reactor conditions in the nuclear industry.Owing to economic and security concerns,a common method is to train data generated by simulators.However,achieving a satisfactory performance in practical applications is difficult because simulators imperfectly emulate reality.To bridge this gap,we propose a novel framework called simulation-to-reality domain adaptation(SRDA)for forecasting the operating parameters of nuclear reactors.The SRDA model employs a transformer-based feature extractor to capture dynamic characteristics and temporal dependencies.A parameter predictor with an improved logarithmic loss function is specifically designed to adapt to varying reactor powers.To fuse prior reactor knowledge from simulations with reality,the domain discriminator utilizes an adversarial strategy to ensure the learning of deep domain-invariant features,and the multiple kernel maximum mean discrepancy minimizes their discrepancies.Experiments on neutron fluxes and temperatures from a pressurized water reactor illustrate that the SRDA model surpasses various advanced methods in terms of predictive performance.This study is the first to use domain adaptation for real-world reactor prediction and presents a feasible solution for enhancing the transferability and generalizability of simulated data.
基金Supported by"Provincial and Ministerial Key Discipline,Provincial Key Laboratory of University and School Laboratory Sharing Platform" ItemSouthwest Forestry University Key Fund Item(110909)~~
文摘[Objective] The research aimed to study the transferability of Camellia sinensis EST-SSRs in Theaceae plant.[Method] Seven pairs of EST-SSRs primers which derived from Camellia sinensis EST sequence were used to amplify the nineteen materials of Theaceae plant.[Result] Five pairs in the seven pairs of EST-SSRs primers could effectively amplify the nineteen tested varieties,and the transferability rate was 71.43%.The amplification rate of Camellia retiacalate FengShanCha,Camellia japouica CaiXia and Camellia retiacalate JuBan was the highest.The amplification rate of Camellia synaptica Sealy and Adinandra sagonica var.wallichiana（oc）Ming was the lowest.Moreover,four pairs in the five pairs of primers which could effectively amplify showed the rich polymorphism whose difference was obvious in the tested materials.[Conclusion] SSR primers which were developed from Camellia sinensis genome had the higher transferability in the different genus and species of Theaceae plant,could be used in the comparative genome research and analysis mark research of Theaceae plant.
文摘Simple sequence repeat （SSR） or microsatellite marker is a valuable tool for several purposes, such as mapping, fingerprinting, and breeding. In the present study, an intersimple sequence repeat （ISSR）-PCR technique was applied for developing SSR markers in non-heading Chinese cabbage （Brassica rapa）. A total of 190 SSRs were obtained. Among these, AG or CT （54.7%） was the most frequent repeat, followed by AC or GT （31.6%） of the microsatellites. The average number of the SSRs length array was 16 and 10 times, respectively. Based on the determined SSR sequences, 143 SSR primer pairs were designed to evaluate their transferabilities among the related species of Brassica. The number of alleles produced per marker averaged 2.91, and the polymorphism information content （PIC） value ranged from 0 to 0.863 with an average of 0.540. Monomorphism was observed in 16 primer pairs. The transferability percentage in CC genome was higher than in BB genome. More loci occurred in the BBCC genome. This result supported the hypothesis that BB genome was divergent from A and C genomes, and AA and CC genomes were relatively close. The polymorphic primers can be exploited for further evolution, fingerprinting, and variety identification.
文摘Currently development of new marker types has shifted from anonymous DNA fragments to gene-based markers. Simple Sequence Repeats (SSRs) are useful DNA markers in plant genetic research including in peanut. However, de novo development of SSRs is expensive and time consuming. Gene-based DNA markers are transferable among related species owing to the conserved nature of genes. In this study transferability of sorghum EST-SSR (SbEST-SSR) markers to peanut was prospected. A set of 411 SbEST-SSR primer pairs were used to amplify peanut genomic DNA extracted from cultivated peanut where 39% of them successfully amplified. A comparison of amplification patterns between sorghum and peanut showed similar banding pattern with majority of transferable SbEST-SSRs. Among these transferable SSR markers, 14% have detected polymorphism among 4 resistant and 4 susceptible peanut lines for rust and late leaf spot diseases. These transferable markers will benefit peanut genome research by not only providing additional DNA markers for population genetic analyses, but also allowing comparative mapping to be possible between peanut and sorghum—a possible monocot-dicot comparison.
文摘We characterized 14 anonymous nuclear loci from Pinus thunbergii Parl., an important pine species native to Japan. One hundred and twenty-six single nucleotide polymorphisms （SNPs） were identified from these loci, giving a frequency of 1 SNP per 51 bp. Nucleotide di- versity （0） ranged from 1.06 × 10^-3 to 11.87 × 10^-3, with all average of 4.99 × 10^-3. Only one locus （mK45） deviated significantly from the Hardy-Weinberg equilibrium. Thirteen of 14 loci were applicable in other pine species. These loci will be useful for nucleotide variation studies and will provide material for SNP-based marker development in P. thun- bergii and related species.
基金supported by the Key Research Program of Frontier Sciences of Chinese Academy of Sciences(Grant No.QYZDB-SSW-SYS024)National Natural Science Foundation of China(Grant Nos.41771081 and 41761134093).
文摘Glacier mass balance, the difference between accumulation and ablation at the glacier surface, is the direct reflection of the local climate regime. Under global warming, the simulation of glacier mass balance at the regional scale has attracted increasing interests. This study selects Urumqi Glacier No. 1 as the testbed for examining the transferability in space and time of two commonly used glacier mass balance simulation models: i.e., the Degree-Day Model(DDM) and the simplified Energy Balance Model(s EBM). Four experiments were carried out for assessing both models’ temporal and spatial transferability. The results show that the spatial transferability of both the DDM and s EBM is strong, whereas the temporal transferability of the DDM is relatively weak. For all four experiments, the overall simulation effect of the s EBM is better than that of the DDM. At the zone around Equilibrium Line Altitude(ELA), the DDM performed better than the s EBM.Also, the accuracy of parameters, including the lapse rate of air temperature and vertical gradient of precipitation at the glacier surface, is of great significance for improving the spatial transferability of both models.
基金This work was supported by Sichuan Science and Technology Program[No.2022YFG0315,2022YFG0174]Sichuan Gas Turbine Research Institute stability support project of China Aero Engine Group Co.,Ltd.[GJCZ-2019-71]Key project of Chengdu[No.2019-YF09-00044-CG].
文摘The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario.However,they often exhibit weak transferability in the black-box scenario,especially when attacking those with defense mechanisms.In this work,we propose a new transfer-based blackbox attack called the channel decomposition attack method(CDAM).It can attack multiple black-box models by enhancing the transferability of the adversarial examples.On the one hand,it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate gradient.On the other hand,it helps to escape from local optima by initializing the data point with random noise.Besides,it could combine with other transfer-based attacks flexibly.Extensive experiments on the standard ImageNet dataset show that our method could significantly improve the transferability of adversarial attacks.Compared with the state-of-the-art method,our approach improves the average success rate from 88.2%to 96.6%when attacking three adversarially trained black-box models,demonstrating the remaining shortcomings of existing deep learning models.
基金The German Academic Exchange Service(DAAD)is appreciated for financial support to the first author
文摘Transferability of five nuclear microsatellite markers (Jc-16, Jc-31, Jc-32, Jc-35 and Jc-37) that were originally developed for J. communis was tested to J. procera. Jc-31 & Jc-37 showed successful amplifications and polymorphism in J. procera. Jc-35 which had been reported as polymorphic in J. communis was monomorphic in J. procera while the primer pair for Jc-32 failed to record any amplification. The remaining one primer pair (Jc-16) showed double loci ampli-fication in both J. procera and the control J. communis suggesting further examination of the primer pair and its binding sites. Genetic variation of six Ethiopian J. procera populations: Chilimo, Goba, Menagesha-Suba, Wef-Washa, Yabelo and Ziquala was assessed based on the two polymorphic loci (Jc-31 & Jc-37) in 20 - 24 individuals of each population. From these two loci, a total of 41 alleles could be retrieved. Two populations that are located south east of the Great Rift Valley together harboured 75% of private alleles signifying their deviant geo-ecological zones and suggesting special consideration for conservation. Chilimo, which is at the western margin of Juniper habitat in Ethiopian central highlands scored the highest fixation (FIS = 0.584) entailing lower immigrant genes and hence higher inbreeding. The AMOVA revealed that 97% of the variation resided within the?population while still among population variation was significant
文摘Safety performance functions(SPFs),or crash-prediction models,have played an important role in identifying the factors contributing to crashes,predicting crash counts and identifying hotspots.Since a great deal of time and effort is needed to estimate an SPF,previous studies have sought to determine the transferability of particular SPFs;that is,the extent to which they can be applied to data from other regions.Although many efforts have been made to examine micro-level SPF transferability,few studies have focused on macro-level SPF transferability.There has been little transferability analysis of macro-level SPFs in the international context,especially between western countries.This study therefore evaluates the transferability of SPFs for several states in the USA(Illinois,Florida and Colorado)and for Italy.The SPFs were developed using data from counties in the United States and provincias in Italy,and the results revealed multiple common significant variables between the two countries.Transferability indexes were then calculated between the SPFs.These showed that the Italy SPFs for total crashes and bicycle crashes were transferable to US data after calibration factors were applied,whereas the US SPFs for total and bicycle crashes,with the exception of the Colorado SPF,could not be transferred to the Italian data.On the other hand,none of the pedestrian SPFs developed was transferable to other countries.This paper provides insights into the applicability of macro-level SPFs between the USA and Italy,and shows a good potential for international SPF transferability.Nevertheless,further investigation is needed of SPF transferability between a wider range of countries.
基金supported by the National Social Science Foundation of China,A Restudy of patent Citation Relationship and its Evaluation Significance from the Perspective of Innovation Economics(Grant No.20XTQ008)。
文摘It is of great significance to study the indicators of university patents’transferability for improving the efficiency of the University Technology Transfer Office and promoting university patent transfer.Based on the in-depth analysis of the existing research,this paper finds that patent quality is the inherent decisive factor of patent transferability.Combining with the evaluation indexes of patent quality and the bibliometrics characteristics of university patents,9 indicators are proposed to indicate the transferability of university patents.Based on the patent transfer data of 35 Chinese universities,this paper analyzes and verifies the potential indicators of patent transfer using the binary logistic regression method.The results show that the number of inventors and the number of non-patent document citations positively predict the transferability of university patents,while the examination duration negatively predicts transferability.The effects of other indicators on transferability need to be discussed considering the actual situation and specific technology fields.
基金the State Key Program of the National Natural Science Foundation of China (Grant No.41530635)the Fund for Innovative Research Group of the National Natural Science Foundation of China (Grant No.51421065)Open Research Fund Program of Key Laboratory of Urban Storm Water System and Water Environment, Ministry of Education.
文摘This study examined the influence of spatial resolution on model parameterization,output,and the parameter transferability between different resolutions using the Storm Water Management Model.High-resolution models,in which most subcatchrnents were homogeneous,and high-resolution-based low-resolution models (in 3 scenarios)were constructed for a highly urbanized catchment in Beijing.The results indicated that the parameterization and simulation results were affected by both spatial resolution and rainfall characteristics.The simulated peak inflow and total runoff volume were sensitive to the spatial resolution,but did not show a consistent tendency.High-resolution models performed very well for both calibration and validation events in terms of three indexes:1)the Nash-Sutcliffe efficiency, 2)the peak flow error,and 3)the volume error;indication of the advantage of using these models.The parameters obtained from high-resolution models could be directly used in the low-resolution models and performed well in the simulation of heavy rain and torrential rain and in the study area where sub-area routing is insignificant.Alternatively,sub-area routing should be considered and estimated approximately.The successful scale conversion from high spatial resolution to low spatial resolution is of great significance for the hydrological simulation of ungauged large areas.
基金This research work is partly supported by the National Natural Science Foundation of China(62172001)the Provincial Colleges Quality Project of Anhui Province(2020xsxxkc047)the National Undergraduate Innovation and Entrepreneurship Training Program(202210357077).
文摘Adding subtle perturbations to an image can cause the classification model to misclassify,and such images are called adversarial examples.Adversar-ial examples threaten the safe use of deep neural networks,but when combined with reversible data hiding(RDH)technology,they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models.Based on this,the reversible adversarial example(RAE)is ris-ing.However,existing RAE technology focuses on feasibility,attack success rate and image quality,but ignores transferability and time complexity.In this paper,we optimize the data hiding structure and combine data augmentation technology,whichflips the input image in probability to avoid overfitting phenomenon on the dataset.On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality,the proposed method improves the transferability of reversible adversarial examples by approximately 16%and reduces the com-putational cost by approximately 43%compared to the state-of-the-art method.In addition,the appropriateflip probability can be selected for different application scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金partially funded by the DAAD Project(No.57474280)Verkehr-SuTra:Technologies for Sustainable Transportation,within the Programme:A New Passage to India—Deutsch-Indische Hochschulkooperationen ab 2019the German Federal Ministry of Education and Research,Bundesministerium für Bildung und Forschung(BMBF),project FuturTrans:Indo-German Collaborative Research Center on Intelligent Transportation Systemsby the European Union's Horizon 2020 research and innovation programme under grant agreement No.815069(project MOMENTUM(Modelling Emerging Transport Solutions for Urban Mobility)).
文摘Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies.Particularly useful for travel analysis is when Social Media users allow(voluntarily or not)tracing their movement using geotagged information of their communication with these online platforms.In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns,study differences and commonalities among these cities,and explore the nature of user location recurrence.The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models.Identification of repeated rates of location(recurrence)can be used to define activity spaces.Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here.The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.
基金supported by the National Nature Science Foundation of China(Nos.52202335 and 52171227)Natural Science Foundation of Jiangsu Province(No.BK20221137)National Key R&D Program of China(2024YFE0108500).
文摘Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique advantages of charge delocalization and enrich uncoordinated electrons and short-range transfer kinetics,which are crucial to achieve rapid low-temperature charge transfer and high-temperature interface stability.Herein,a quantum-scale FeS_(2) loaded on three-dimensional Ti_(3)C_(2) MXene skeletons(FeS_(2) QD/MXene)fabricated as SIBs anode,demonstrating impressive performance under wide-temperature conditions(−35 to 65).The theoretical calculations combined with experimental characterization interprets that the unsaturated coordination edges of FeS_(2) QD can induce delocalized electronic regions,which reduces electrostatic potential and significantly facilitates efficient Na+diffusion across a broad temperature range.Moreover,the Ti_(3)C_(2) skeleton reinforces structural integrity via Fe-O-Ti bonding,while enabling excellent dispersion of FeS_(2) QD.As expected,FeS_(2) QD/MXene anode harvests capacities of 255.2 and 424.9 mAh g^(−1) at 0.1 A g^(−1) under−35 and 65,and the energy density of FeS_(2) QD/MXene//NVP full cell can reach to 162.4 Wh kg^(−1) at−35,highlighting its practical potential for wide-temperatures conditions.This work extends the uncoordinated regions induced by quantum-size effects for exceptional Na^(+)ion storage and diffusion performance at wide-temperatures environment.
基金supported by The University of Hong Kong,China(109000487,109001694,204610401,and 204610519)National Natural Science Foundation of China(82402225)(to JH).
文摘Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke.In recent years,the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation.This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer’s disease,Parkinson’s disease,multiple sclerosis,and Huntington’s disease.A comprehensive literature search was conducted using databases such as PubMed and Google Scholar,focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications.The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism,which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases.Although promising results have been observed in selected clinical and preclinical trials,further validations are needed to evaluate their clinical value.When combined with other imaging modalities and advanced analytical methods,chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker,enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
基金The development of the entropy maximization method and the generation of the training data was supported by the Exascale Computing Project(17-SC-20-SC),a collaborative effort of the U.SDepartment of Energy Office of Science and the National Nuclear Security Administration.The training of the various MLIAP models and the comparative performance analysis was supported by the U.S.Department of Energy,Office of Fusion Energy Sciences(OFES)under Field Work Proposal Number 20-023149+1 种基金Sandia National Laboratories is a multimission laboratory managed and operated by National Technology&Engineering Solutions of Sandia,LLC,a wholly owned subsidiary of Honeywell International Inc.,for the U.S.Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525Los Alamos National Laboratory is operated by Triad National Security LLC,for the National Nuclear Security administration of the U.S.DOE under Contract No.89233218CNA0000001.
文摘Advances in machine learning(ML)have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost,parallel efficiency of empirical potentials.However,ML-based potentials struggle to achieve transferability,i.e.,provide consistent accuracy across configurations that differ from those used during training.In order to realize the promise of ML-based potentials,systematic and scalable approaches to generate diverse training sets need to be developed.This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach.Subsequently,multiple polynomial and neural network potentials are trained on the entropy-optimized dataset.A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison.The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models.Furthermore,the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.
