The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a crit...The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal ro...Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.展开更多
Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evalu...BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.展开更多
Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is emp...Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.展开更多
BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influenc...BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influencing attention bias,specifically identifying the aspects of attention processes affected by hypnosis.METHODS In this study,69 SAD participants were divided into three groups based on their Liebowitz Social Anxiety Scale scores:Experimental group,control group,and baseline group.The experimental group(n=23)underwent six weekly hypnosis sessions,while the control(n=23)and baseline groups(n=23)received no treatment.To evaluate alterations in SAD severity and attention bias towards threatening stimuli following hypnotherapy,we employed a combination of self-report questionnaires,an odd-one-out task,and electroencephalography recordings.RESULTS The experimental group showed significant reductions in P1,N170,N2pc,and late positive potential(LPP)brain wave activities during attention sensitivity and disengagement conditions.This indicates that hypnotherapy modulates early-stage face processing and later-stage emotional evaluation of threat-related stimuli in SAD patients.CONCLUSION Our findings highlight P1,N170,N2pc,and LPP as key neural markers in SAD treatment.By identifying these neural markers influenced by hypnotherapy,we offer insight into the mechanisms by which this treatment modality impacts attentional processes,potentially easing SAD symptoms.展开更多
Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecas...Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model(ECMWF-IFS)over 14 offshore weather stations along the coast of Shandong Province,this study introduces a multi-task learning(MTL)model(TabNet-MTL),which significantly improves the forecast bias of near-surface wind direction and speed simultaneously.TabNet-MTL adopts the feature engineering method,utilizes mean square error as the loss function,and employs the 5-fold cross validation method to ensure the generalization ability of the trained model.It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version(TabNet-STL)and three other popular single-task learning models(Random Forest,LightGBM,and XGBoost).Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s−1,and increases the forecast accuracy of wind direction from 50%to 65%.As an explainable deep learning model,the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering.展开更多
Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine lea...Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.展开更多
Uneven terrain significantly increases the seismic risk of tunnels in loess deposits.To investigate the variations in optimal intensity measures(IMs)for shallowly buried loess tunnels considering biased terrain,nonlin...Uneven terrain significantly increases the seismic risk of tunnels in loess deposits.To investigate the variations in optimal intensity measures(IMs)for shallowly buried loess tunnels considering biased terrain,nonlinear dynamic analyses were conducted to obtain seismic responses validated by the actual damage pattern.Then IMs were evaluated based on the automatic calculation of the time history damage index fulfilled by a compiled Python program.Results showed that the plastic strain zone progressively developed and extended from the vault to the central slope surface with increasing seismic intensities,ultimately causing shear failure to the tunnel.For IMs at the slope top,peak ground velocity(PGV)(ζ=0.15),velocity spectrum intensity(VSI)(ζ=0.20),and peak spectrum velocity(PSv)(ζ=0.22)were all suitable for seismic fragility assessment.The VSI(ζ=0.17)was optimal,followed by PGV(ζ=0.19)and PSv(ζ=0.2)for those at the slope foot.Acceleration-related IMs were more sensitive to terrain variation.Comparative analyses demonstrated smaller damage probabilities for the IMs at the slope top than those at the slope foot under the same intensity level.The impact of unfavorable terrain on tunnels was accentuated as those located in uneven mountainous regions became more vulnerable to ground shaking.展开更多
We investigate the surface acoustic wave(SAW)modulation of the exchange bias field(H_(EB))in Py/IrMn films deposited on LiNbO_(3)substrates.We measured the anisotropic magnetoresistance(AMR)of the multilayer film when...We investigate the surface acoustic wave(SAW)modulation of the exchange bias field(H_(EB))in Py/IrMn films deposited on LiNbO_(3)substrates.We measured the anisotropic magnetoresistance(AMR)of the multilayer film when continuous SAW or pulsed SAW were applied and obtained H_(EB).With continuous SAW,the H_(EB)decreases continuously with power.While in the case of pulsed SAW,the H_(EB)first decreases and then stabilizes.Compared to pulsed SAW,the thermal effects from the continuous SAW lead to the continuous decrease of H_(EB)at higher SAW power,which is verified by the measurement of H_(EB)at different temperatures and input currents.Furthermore,our results show that pulsed SAW can effectively avoid thermal effects.The decrease of H_(EB)at smaller power in both continuous and pulsed SAW is mainly due to the SAW-induced dynamic strain field,which leads to a small perturbation in the magnetic moment of the FM layer.Combined with the AMR values measured at different angles during the saturation field,we believe that the SAW-induced dynamic strain field causes a 15°angle between the magnetic moment and the easy axis.Our experiments provide a different approach to manipulating H_(EB),opening up a potential avenue for future manipulation of antiferromagnetic moments.展开更多
Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The...Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.展开更多
Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate...Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.展开更多
In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean inten...In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.展开更多
This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(f...This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(females=22,Mage=19.44,SD=1.36)and 35 with a low sense of control(females=15,Mage=19.77,SD=1.40)were assigned to task featuring different cue-target intervals(i.e.,50 and 800 ms).The student participants completed the Control Sense Scale,the GAD-7 Anxiety Scale,and the PHQ-9 Patient Health Questionnaire.Data from employing spatial-cueing task procedure,would provide the evidence on any differences in attentional biases toward threat images between the two groups.A repeated measures ANOVA indicated that both groups to exhibit attentional avoidance under the 50 ms interval condition.However,individuals in the low sense of control group(i.e.,LSC Group)demonstrated exacerbation of avoidance compared to those in the high sense of control group(i.e.,HSC Group).The current study did notfind any attentional bias components under the 800 ms interval condition.Thefindings provide preliminary evidence for a new vigilance-avoidance model for further study with a view to developing interventions targeting negative emotional disorders based on individuals’sense of control.展开更多
In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(M...In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.展开更多
The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the e...The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the electric field or the current profile.To this end,two sets of electrodes have been designed and installed on the Keda Torus eXperiment(KTX)RFP device to manipulate the edge electric field and the edge parallel current profile.Subsequently,the edge radial electric field and edge parallel current profile control experiments are conducted.In the edge radial electric field control experiments,the edge radial electric field is altered under bias,accompanied with an increase in the electron density and plasma duration.However,under bias,both electrostatic and magnetic fluctuations are enhanced.In the edge parallel current profile control experiments,the results indicate that bias modifies the edge parallel current profile locally,leading to a localized increase in the field reversal depth and electron density.Additionally,a reduction in magnetic fluctuations is observed within the reversed field enhanced region under bias,suggesting that the bias suppresses magnetic perturbations.展开更多
Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,w...Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,which face a challenge of precise decision-making in many fields.To address this issue,we conduct a theoretical analysis of the underestimation bias and propose the minmax operation,which allow for flexible control of the estimation bias.Specifically,we select the maximum value of each action from multiple parallel state-action networks to create a new state-action value sequence.Then,a minimum value is selected to obtain more accurate value estimations.Moreover,based on the minmax operation,we propose two novel algorithms by combining Deep Q-Network(DQN)and Double DQN(DDQN),named minmax-DQN and minmax-DDQN.Meanwhile,we conduct theoretical analyses of the estimation bias and variance caused by our proposed minmax operation,which show that this operation significantly improves both underestimation and overestimation biases and leads to the unbiased estimation.Furthermore,the variance is also reduced,which is helpful to improve the network training stability.Finally,we conduct numerous comparative experiments in various environments,which empirically demonstrate the superiority of our method.展开更多
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
文摘The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金supported by the National Key R&D Program of the Ministry of Science and Technology(2023YFC2509900)National Natural Science Foundation of China(82374106)+3 种基金National Natural Science Foundation of China(U22A20371)the Basic and Applied Basic Research Fund of Guangdong Province(2021B1515120061)the Shenzhen Science and Technology Innovation Committee(JCYJ20210324102006017)SZ-HK Joint Laboratory for Innovative Biomaterials under CAS-HK Joint Laboratories(2024-2028).
文摘Osteoarthritis(OA)is a prevalent degenerative joint disorder marked by chronic pain,inflammation,and cartilage loss,with current treatments limited to symptom relief.G protein-coupled receptors(GPCRs)play a pivotal role in OA progression by regulating inflammation,chondrocyte survival,and matrix homeostasis.However,their multifaceted signaling,via G proteins orβ-arrestins,poses challenges for precise therapeutic targeting.Biased agonism,where ligands selectively activate specific GPCR pathways,emerges as a promising approach to optimize efficacy and reduce side effects.This review examines biased signaling in OAassociated GPCRs,including cannabinoid receptors(CB1,CB2),chemokine receptors(CCR2,CXCR4),protease-activated receptors(PAR-2),adenosine receptors(A1R,A2AR,A2BR,A3R),melanocortin receptors(MC1R,MC3R),bradykinin receptors(B2R),prostaglandin E2 receptors(EP-2,EP-4),and calcium-sensing receptors(CaSR).We analyze ligands in clinical trials and explore natural products from Traditional Chinese Medicine as potential biased agonists.These compounds,with diverse structures and bioactivities,offer novel therapeutic avenues.By harnessing biased agonism,this review underscores the potential for developing targeted,safer OA therapies that address its complex pathology,bridging molecular insights with clinical translation.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
文摘BACKGROUND Transcatheter arterial chemoembolization(TACE)is a key treatment approach for advanced invasive liver cancer(infiltrative hepatocellular carcinoma).However,its therapeutic response can be difficult to evaluate accurately using conventional two-dimensional imaging criteria due to the tumor’s diffuse and multifocal growth pattern.Volumetric imaging,especially enhanced tumor volume(ETV),offers a more comprehensive assessment.Nonetheless,bias field inhomogeneity in magnetic resonance imaging(MRI)poses challenges,potentially skewing volumetric measurements and undermining prognostic evaluation.AIM To investigate whether MRI bias field correction enhances the accuracy of volumetric assessment of infiltrative hepatocellular carcinoma treated with TACE,and to analyze how this improved measurement impacts prognostic prediction.METHODS We retrospectively collected data from 105 patients with invasive liver cancer who underwent TACE treatment at the Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2024.The improved N4 bias field correction algorithm was applied to process MRI images,and the ETV before and after treatment was calculated.The ETV measurements before and after correction were compared,and their relationship with patient prognosis was analyzed.A Cox proportional hazards model was used to evaluate prognostic factors,with Martingale residual analysis determining the optimal cutoff value,followed by survival analysis.RESULTS Bias field correction significantly affected ETV measurements,with the corrected baseline ETV mean(505.235 cm^(3))being significantly lower than before correction(825.632 cm^(3),P<0.001).Cox analysis showed that the hazard ratio(HR)for corrected baseline ETV(HR=1.165,95%CI:1.069-1.268)was higher than before correction(HR=1.063,95%CI:1.031-1.095).Using 412 cm^(3) as the cutoff,the group with baseline ETV<415 cm^(3) had a longer median survival time compared to the≥415 cm^(3) group(18.523 months vs 8.926 months,P<0.001).The group with an ETV reduction rate≥41%had better prognosis than the<41%group(17.862 months vs 9.235 months,P=0.006).Multivariate analysis confirmed that ETV reduction rate(HR=0.412,P<0.001),Child-Pugh classification(HR=0.298,P<0.001),and Barcelona Clinic Liver Cancer stage(HR=0.578,P=0.045)were independent prognostic factors.CONCLUSION Volume imaging based on MRI bias field correction can improve the accuracy of evaluating the efficacy of TACE treatment for invasive liver cancer.The corrected ETV and its reduction rate can serve as independent indicators for predicting patient prognosis,providing important reference for developing individualized treatment strategies.
文摘Cognitive bias,stemming from electronic measurement error and variability in human perception,exists in cognitive electronic warfare and affects the outcomes of conflicts.In this paper,the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures.The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B.With perfect Bayesian equilibrium,a dynamic radar countermeasure model is established,and the impact of cognitive bias is analyzed.Drawing inspiration from the cognitive bias analysis method used in stock market trading,a cognitive bias model for cognitive radar countermeasures is introduced,and its correctness is mathematically proved.A gaming scenario involving the AN/SPY-1 radar and a smart jammer is set up to analyze the influence of cognitive bias on game outcomes.Simulation results validate the effectiveness of the proposed method.
基金Supported by National Natural Science Foundation of China,No.82090034the Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention,No.SYS2023B08the Anhui Natural Science Foundation,No.2023AH040086.
文摘BACKGROUND Exploring hypnotherapy's potential to modulate attention bias offers promising avenues for treating social anxiety disorder(SAD).AIM To determine if hypnotherapy can alleviate social anxiety by influencing attention bias,specifically identifying the aspects of attention processes affected by hypnosis.METHODS In this study,69 SAD participants were divided into three groups based on their Liebowitz Social Anxiety Scale scores:Experimental group,control group,and baseline group.The experimental group(n=23)underwent six weekly hypnosis sessions,while the control(n=23)and baseline groups(n=23)received no treatment.To evaluate alterations in SAD severity and attention bias towards threatening stimuli following hypnotherapy,we employed a combination of self-report questionnaires,an odd-one-out task,and electroencephalography recordings.RESULTS The experimental group showed significant reductions in P1,N170,N2pc,and late positive potential(LPP)brain wave activities during attention sensitivity and disengagement conditions.This indicates that hypnotherapy modulates early-stage face processing and later-stage emotional evaluation of threat-related stimuli in SAD patients.CONCLUSION Our findings highlight P1,N170,N2pc,and LPP as key neural markers in SAD treatment.By identifying these neural markers influenced by hypnotherapy,we offer insight into the mechanisms by which this treatment modality impacts attentional processes,potentially easing SAD symptoms.
基金the National Key Research and Development Plan of China[Grant No.2023YFB3002400]the Shanghai 2021 Natural Science Foundation[Grant Nos.21ZR1420400 and 21ZR1419800]+1 种基金the Shanghai 2023 Natural Science Foundation[Grant No.23ZR1463000]the Shandong Provincial Meteorological Bureau Scientific Research Project[Grant No.2023SDBD05].
文摘Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model(ECMWF-IFS)over 14 offshore weather stations along the coast of Shandong Province,this study introduces a multi-task learning(MTL)model(TabNet-MTL),which significantly improves the forecast bias of near-surface wind direction and speed simultaneously.TabNet-MTL adopts the feature engineering method,utilizes mean square error as the loss function,and employs the 5-fold cross validation method to ensure the generalization ability of the trained model.It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version(TabNet-STL)and three other popular single-task learning models(Random Forest,LightGBM,and XGBoost).Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s−1,and increases the forecast accuracy of wind direction from 50%to 65%.As an explainable deep learning model,the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering.
文摘Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.
基金supported by the National Natural Science Foundation of China(Grant numbers 52208392,52068044,and 52168058)China Post-doctoral Science Foundation(Grant number 2021M693843)+1 种基金Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University(Grant number 1520260306)Key Laboratory of Road and Bridge and Underground Engineering of Gansu Province(Grant number GSDQ-KF2020-5).
文摘Uneven terrain significantly increases the seismic risk of tunnels in loess deposits.To investigate the variations in optimal intensity measures(IMs)for shallowly buried loess tunnels considering biased terrain,nonlinear dynamic analyses were conducted to obtain seismic responses validated by the actual damage pattern.Then IMs were evaluated based on the automatic calculation of the time history damage index fulfilled by a compiled Python program.Results showed that the plastic strain zone progressively developed and extended from the vault to the central slope surface with increasing seismic intensities,ultimately causing shear failure to the tunnel.For IMs at the slope top,peak ground velocity(PGV)(ζ=0.15),velocity spectrum intensity(VSI)(ζ=0.20),and peak spectrum velocity(PSv)(ζ=0.22)were all suitable for seismic fragility assessment.The VSI(ζ=0.17)was optimal,followed by PGV(ζ=0.19)and PSv(ζ=0.2)for those at the slope foot.Acceleration-related IMs were more sensitive to terrain variation.Comparative analyses demonstrated smaller damage probabilities for the IMs at the slope top than those at the slope foot under the same intensity level.The impact of unfavorable terrain on tunnels was accentuated as those located in uneven mountainous regions became more vulnerable to ground shaking.
基金supported by the National Natural Science Foundation of China(Grant Nos.12174166 and 12304144)the Fund from Beijing National Laboratory for Condensed Matter Physics(Grant No.2024BNLCMPKF013)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-22).
文摘We investigate the surface acoustic wave(SAW)modulation of the exchange bias field(H_(EB))in Py/IrMn films deposited on LiNbO_(3)substrates.We measured the anisotropic magnetoresistance(AMR)of the multilayer film when continuous SAW or pulsed SAW were applied and obtained H_(EB).With continuous SAW,the H_(EB)decreases continuously with power.While in the case of pulsed SAW,the H_(EB)first decreases and then stabilizes.Compared to pulsed SAW,the thermal effects from the continuous SAW lead to the continuous decrease of H_(EB)at higher SAW power,which is verified by the measurement of H_(EB)at different temperatures and input currents.Furthermore,our results show that pulsed SAW can effectively avoid thermal effects.The decrease of H_(EB)at smaller power in both continuous and pulsed SAW is mainly due to the SAW-induced dynamic strain field,which leads to a small perturbation in the magnetic moment of the FM layer.Combined with the AMR values measured at different angles during the saturation field,we believe that the SAW-induced dynamic strain field causes a 15°angle between the magnetic moment and the easy axis.Our experiments provide a different approach to manipulating H_(EB),opening up a potential avenue for future manipulation of antiferromagnetic moments.
基金supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LL.Z012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901).
文摘Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.
基金supported by the National Natural Science Foundation of China[grant number 42275074].
文摘Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1532113,11475170,11905041)Anhui Provincial Natural Science Foundation(Grant No.2208085MA18)Fundamental Research Funds for the Central Universities(Grant No.JZ2022HGTB0244)。
文摘In x-ray dark-field imaging using dual phase grating interferometer,multi-contrast signals are extracted from a set of acquired phase-stepping data by using the least-squares fitting algorithm.The extracted mean intensity,amplitude and visibility signals may be intrinsically biased.However,it is still unclear how large these biases are and how the data acquisition parameters influence the biases in the extracted signals.This work set out to address these questions.Analytical expressions of the biases of the extracted signals were theoretically derived by using a second-order Taylor series expansion.Extensive numerical simulations were performed to validate the theoretical results.It is illustrated that while the estimated mean intensity signal is always unbiased,the estimated amplitude and visibility signals are both positively biased.While the biases of the estimated amplitude signals are proportional to the inverse of the total number of phase steps,the biases of the estimated visibility signals are inversely proportional to the product of the total number of phase steps and the mean number of photons counted per phase step.Meanwhile,it is demonstrated that the dependence of the biases on the mean visibility is quite different from that of Talbot-Lau interferometer due to the difference in the intensity model.We expect that these results can be useful for data acquisition optimizations and interpretation of x-ray dark-field images.
基金supported by the Philosophy and Social Science Fund for Young Scholars of Guangdong Province(GD23YXL06)Humanities and Social Sciences of Jiaying University(2023SKY01)+1 种基金General Project of Philosophy and Social Sciences Planning Fund of Guangdong Province(GD24XXL06)Humanities and Social Sciences of Jiaying University(2023SKY02).
文摘This study investigated how components of threat-related attentional biases are associated with levels of sense of control.Utilizing a using a spatial-cueing paradigm,36 college students with a high sense of control(females=22,Mage=19.44,SD=1.36)and 35 with a low sense of control(females=15,Mage=19.77,SD=1.40)were assigned to task featuring different cue-target intervals(i.e.,50 and 800 ms).The student participants completed the Control Sense Scale,the GAD-7 Anxiety Scale,and the PHQ-9 Patient Health Questionnaire.Data from employing spatial-cueing task procedure,would provide the evidence on any differences in attentional biases toward threat images between the two groups.A repeated measures ANOVA indicated that both groups to exhibit attentional avoidance under the 50 ms interval condition.However,individuals in the low sense of control group(i.e.,LSC Group)demonstrated exacerbation of avoidance compared to those in the high sense of control group(i.e.,HSC Group).The current study did notfind any attentional bias components under the 800 ms interval condition.Thefindings provide preliminary evidence for a new vigilance-avoidance model for further study with a view to developing interventions targeting negative emotional disorders based on individuals’sense of control.
文摘In this article,we comment on the article by Long et al published in the recent issue of the World Journal of Gastrointestinal Oncology.Rectal cancer patients are at risk for developing metachronous liver metastasis(MLM),yet early prediction remains challenging due to variations in tumor heterogeneity and the limitations of traditional diagnostic methods.Therefore,there is an urgent need for noninvasive techniques to improve patient outcomes.Long et al’s study introduces an innovative magnetic resonance imaging(MRI)-based radiomics model that integrates high-throughput imaging data with clinical variables to predict MLM.The study employed a 7:3 split to generate training and validation datasets.The MLM prediction model was constructed using the training set and subsequently validated on the validation set using area under the curve(AUC)and dollar-cost averaging metrics to assess performance,robustness,and generalizability.By employing advanced algorithms,the model provides a non-invasive solution to assess tumor heterogeneity for better metastasis prediction,enabling early intervention and personalized treatment planning.However,variations in MRI parameters,such as differences in scanning resolutions and protocols across facilities,patient heterogeneity(e.g.,age,comorbidities),and external factors like carcinoembryonic antigen levels introduce biases.Additionally,confounding factors such as diagnostic staging methods and patient comorbidities require further validation and adjustment to ensure accuracy and generalizability.With evolving Food and Drug Administration regulations on machine learning models in healthcare,compliance and careful consideration of these regulatory requirements are essential to ensuring safe and effective implementation of this approach in clinical practice.In the future,clinicians may be able to utilize datadriven,patient-centric artificial intelligence(AI)-enhanced imaging tools integrated with clinical data,which would help improve early detection of MLM and optimize personalized treatment strategies.Combining radiomics,genomics,histological data,and demographic information can significantly enhance the accuracy and precision of predictive models.
基金supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2022YFE03100004,2017YFE0301700 and 2017 YFE0301701)National Natural Science Foundation of China(Nos.12375226,11875255,11635008,11375188 and 11975231)。
文摘The electromagnetic turbulence in reversed field pinch(RFP)plasmas exhibits three-dimensional characteristics.Suppression of this turbulence is crucial for enhancing plasma confinement,necessitating control over the electric field or the current profile.To this end,two sets of electrodes have been designed and installed on the Keda Torus eXperiment(KTX)RFP device to manipulate the edge electric field and the edge parallel current profile.Subsequently,the edge radial electric field and edge parallel current profile control experiments are conducted.In the edge radial electric field control experiments,the edge radial electric field is altered under bias,accompanied with an increase in the electron density and plasma duration.However,under bias,both electrostatic and magnetic fluctuations are enhanced.In the edge parallel current profile control experiments,the results indicate that bias modifies the edge parallel current profile locally,leading to a localized increase in the field reversal depth and electron density.Additionally,a reduction in magnetic fluctuations is observed within the reversed field enhanced region under bias,suggesting that the bias suppresses magnetic perturbations.
基金supported by the National Natural Science Foundation of China(No.62173272).
文摘Obtaining the accurate value estimation and reducing the estimation bias are the key issues in reinforcement learning.However,current methods that address the overestimation problem tend to introduce underestimation,which face a challenge of precise decision-making in many fields.To address this issue,we conduct a theoretical analysis of the underestimation bias and propose the minmax operation,which allow for flexible control of the estimation bias.Specifically,we select the maximum value of each action from multiple parallel state-action networks to create a new state-action value sequence.Then,a minimum value is selected to obtain more accurate value estimations.Moreover,based on the minmax operation,we propose two novel algorithms by combining Deep Q-Network(DQN)and Double DQN(DDQN),named minmax-DQN and minmax-DDQN.Meanwhile,we conduct theoretical analyses of the estimation bias and variance caused by our proposed minmax operation,which show that this operation significantly improves both underestimation and overestimation biases and leads to the unbiased estimation.Furthermore,the variance is also reduced,which is helpful to improve the network training stability.Finally,we conduct numerous comparative experiments in various environments,which empirically demonstrate the superiority of our method.
