In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Lar...In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.展开更多
BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affectin...BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affecting the alignment,such as ankle joint alignment,should be considered.AIM To determine CPAK distribution in the North African(Egyptian)population with knee osteoarthritis and to assess ankle joint line orientation(AJLO)adaptations across different CPAK types.METHODS A cross-sectional study was conducted on patients with primary knee osteoarthritis and normal ankle joints.Radiographic parameters included the mechanical lateral distal femoral angle,medial proximal tibial angle,and the derived calculations of joint line obliquity(JLO)and arithmetic hip-knee-ankle angle(aHKA).The tibial plafond horizontal angle(TPHA)was used for AJLO assessment,where 0°is neutral(type N),<0°is varus(type A),and>0°is valgus(type B).The nine CPAK types were further divided into 27 subtypes after incorporating the three AJLO types.RESULTS A total of 527 patients(1054 knees)were included for CPAK classification,and 435 patients(870 knees and ankles)for AJLO assessment.The mean age was 57.2±7.8 years,with 79.5%females.Most knees(76.4%)demonstrated varus alignment(mean aHKA was-5.51°±4.84°)and apex distal JLO(55.3%)(mean JLO was 176.43°±4.53°).CPAK types I(44.3%),IV(28.6%),and II(10%)were the most common.Regarding AJLO,70.2%of ankles exhibited varus orientation(mean TPHA was-5.21°±6.45°).The most frequent combined subtypes were CPAK type I-A(33.7%),IV-A(21.5%),and I-N(6.9%).A significant positive correlation was found between the TPHA and aHKA(r=0.40,P<0.001).CONCLUSION In this North African cohort,varus knee alignment with apex distal JLO and varus AJLO predominated.CPAK types I,IV,and II were the most common types,while subtypes I-A,IV-A,and I-N were commonly occurring after incorporating AJLO types;furthermore,the AJLO was significantly correlated to aHKA.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
A polarization-sensitive and flexible photodetector was fabricated through the precise alignment of perovskite nanowires(NWs)using a brush coating technique.The alignment of the NWs was meticulously examined,consideri...A polarization-sensitive and flexible photodetector was fabricated through the precise alignment of perovskite nanowires(NWs)using a brush coating technique.The alignment of the NWs was meticulously examined,considering various chemical properties of the solvent,such as boiling point,viscosity,and surface tension.Notably,when the NWs were brush-coated with toluene dispersion,the NWs were aligned in higher order than those processed from octane dispersion.The degree of alignment was correlated with the photodetector property.Especially,the well-aligned NW photodetector exhibited a two-fold disparity in current response contingent on the polarization direction.Furthermore,even after enduring 500 bending cycles,the device retained 80%of its photodetector performance.This approach underscores the potential of solution-processed flexible photodetectors for advanced optical applications under dynamic operating conditions.展开更多
Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide i...Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting ...The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting atomic-level ab initio calculations on realistic Si/SiO_(2)/HfO_(2)stacks,we reveal the physical origin of band alignment fluctuations,i.e.,the oxygen density-dependent interface and surface dipoles,and demonstrate that band offsets can be tuned without introducing other materials.This is instructive for reducing the gate tunneling current,alleviating device-to-device variation,and tuning the threshold voltage.Additionally,this study indicates that significant attention should be focused on model construction in emerging atomistic studies on semiconductor devices.展开更多
This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in So...This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.展开更多
A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminate...A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.展开更多
Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and t...Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and the modality fusion approach tends to be too simple,often neglecting modality alignment before fusion.This research introduces a novel dual stream multimodal alignment and fusion network named DMAFNet for classifying short videos.The network uses two unimodal encoder modules to extract features within modalities and exploits a multimodal encoder module to learn interaction between modalities.To solve the modality alignment problem,contrastive learning is introduced between two unimodal encoder modules.Additionally,masked language modeling(MLM)and video text matching(VTM)auxiliary tasks are introduced to improve the interaction between video frames and text modalities through backpropagation of loss functions.Diverse experiments prove the efficiency of DMAFNet in multimodal video classification tasks.Compared with other two mainstream baselines,DMAFNet achieves the best results on the 2022 WeChat Big Data Challenge dataset.展开更多
As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limi...As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.展开更多
Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatmen...Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.展开更多
Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing...Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.展开更多
Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing...Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.展开更多
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to...Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.展开更多
Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior...Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.展开更多
Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancement...Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancements in the development of diverse Bi_(2)O_(2)Se heterojunctions have unveiled extensive potential applications in both electronics and optoelectronics.However,achieving an in-depth understanding of band alignment and particularly interface dynamics remains a significant challenge.In this study,we conduct a comprehensive experimental investigation into band alignment utilizing high-resolution X-ray photoelectron spectroscopy(HRXPS),while also thoroughly discussing the properties of interface states.Our findings reveal that ultrathin films of Bi_(2)O_(2)Se grown on SrTiO_(3)(with TiO_(2)(001)termination)exhibit Type-I(straddling gap)band alignment characterized by a valence band offset(VBO)of approximately 1.77±0.04 eV and a conduction band offset(CBO)around 0.68±0.04 eV.Notably,when accounting for the influence of interface states,the bands at the interface display a herringbone configuration due to substantial built-in electric fields,which markedly deviate from conventional band alignments.Thus,our results provide valuable insights for advancing high-efficiency electronic and optoelectronic devices,particularly those where charge transfer is highly sensitive to interface states.展开更多
基金supported by Beijing Insititute of Technology Research Fund Program for Young Scholars(2020X04104)。
文摘In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.
基金approved by Institutional Review Board of Faculty of Medicine in Assiut University,No.04-2024-300470.
文摘BACKGROUND In an era leaning toward a personalized alignment of total knee arthroplasty,coronal plane alignment of the knee(CPAK)phenotypes for each population are studied;furthermore,other possible variables affecting the alignment,such as ankle joint alignment,should be considered.AIM To determine CPAK distribution in the North African(Egyptian)population with knee osteoarthritis and to assess ankle joint line orientation(AJLO)adaptations across different CPAK types.METHODS A cross-sectional study was conducted on patients with primary knee osteoarthritis and normal ankle joints.Radiographic parameters included the mechanical lateral distal femoral angle,medial proximal tibial angle,and the derived calculations of joint line obliquity(JLO)and arithmetic hip-knee-ankle angle(aHKA).The tibial plafond horizontal angle(TPHA)was used for AJLO assessment,where 0°is neutral(type N),<0°is varus(type A),and>0°is valgus(type B).The nine CPAK types were further divided into 27 subtypes after incorporating the three AJLO types.RESULTS A total of 527 patients(1054 knees)were included for CPAK classification,and 435 patients(870 knees and ankles)for AJLO assessment.The mean age was 57.2±7.8 years,with 79.5%females.Most knees(76.4%)demonstrated varus alignment(mean aHKA was-5.51°±4.84°)and apex distal JLO(55.3%)(mean JLO was 176.43°±4.53°).CPAK types I(44.3%),IV(28.6%),and II(10%)were the most common.Regarding AJLO,70.2%of ankles exhibited varus orientation(mean TPHA was-5.21°±6.45°).The most frequent combined subtypes were CPAK type I-A(33.7%),IV-A(21.5%),and I-N(6.9%).A significant positive correlation was found between the TPHA and aHKA(r=0.40,P<0.001).CONCLUSION In this North African cohort,varus knee alignment with apex distal JLO and varus AJLO predominated.CPAK types I,IV,and II were the most common types,while subtypes I-A,IV-A,and I-N were commonly occurring after incorporating AJLO types;furthermore,the AJLO was significantly correlated to aHKA.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金supported by a Commercialization Promotion Agency for R&D Outcomes(COMPA)Grant funded by the Korean Government(Ministry of Science and ICT)(No.RS-2023-00304743)the National Research Foundation of Korea(NRF)Grant funded by the Korean Government(MSIT)(No.2022M3J7A1066428)"Regional Innovation Strategy(RIS)"through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(No.2023RIS-008).
文摘A polarization-sensitive and flexible photodetector was fabricated through the precise alignment of perovskite nanowires(NWs)using a brush coating technique.The alignment of the NWs was meticulously examined,considering various chemical properties of the solvent,such as boiling point,viscosity,and surface tension.Notably,when the NWs were brush-coated with toluene dispersion,the NWs were aligned in higher order than those processed from octane dispersion.The degree of alignment was correlated with the photodetector property.Especially,the well-aligned NW photodetector exhibited a two-fold disparity in current response contingent on the polarization direction.Furthermore,even after enduring 500 bending cycles,the device retained 80%of its photodetector performance.This approach underscores the potential of solution-processed flexible photodetectors for advanced optical applications under dynamic operating conditions.
基金supported by the National Key R&D Program of China(No.2021YFA1501002)Thousand Talents Program for Distinguished Young Scholars.X.Li thanks the National Natural Science Foundation of China(No.22309021).
文摘Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
基金supported by the National Natural Science Foundation of China(Grant Nos.62174155,12334005,and T2293702)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-056)the MIND Project(Grant No.MINDKT202403)。
文摘The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting atomic-level ab initio calculations on realistic Si/SiO_(2)/HfO_(2)stacks,we reveal the physical origin of band alignment fluctuations,i.e.,the oxygen density-dependent interface and surface dipoles,and demonstrate that band offsets can be tuned without introducing other materials.This is instructive for reducing the gate tunneling current,alleviating device-to-device variation,and tuning the threshold voltage.Additionally,this study indicates that significant attention should be focused on model construction in emerging atomistic studies on semiconductor devices.
文摘This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.
基金Supported by Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH040010).
文摘A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.
基金Fundamental Research Funds for the Central Universities,China(No.2232021A-10)National Natural Science Foundation of China(No.61903078)+1 种基金Shanghai Sailing Program,China(No.22YF1401300)Natural Science Foundation of Shanghai,China(No.20ZR1400400)。
文摘Video classification is an important task in video understanding and plays a pivotal role in intelligent monitoring of information content.Most existing methods do not consider the multimodal nature of the video,and the modality fusion approach tends to be too simple,often neglecting modality alignment before fusion.This research introduces a novel dual stream multimodal alignment and fusion network named DMAFNet for classifying short videos.The network uses two unimodal encoder modules to extract features within modalities and exploits a multimodal encoder module to learn interaction between modalities.To solve the modality alignment problem,contrastive learning is introduced between two unimodal encoder modules.Additionally,masked language modeling(MLM)and video text matching(VTM)auxiliary tasks are introduced to improve the interaction between video frames and text modalities through backpropagation of loss functions.Diverse experiments prove the efficiency of DMAFNet in multimodal video classification tasks.Compared with other two mainstream baselines,DMAFNet achieves the best results on the 2022 WeChat Big Data Challenge dataset.
基金supported by National Natural Science Foundation of China(Nos.62477026,62177029,61807020)Humanities and Social Sciences Research Program of the Ministry of Education of China(No.23YJAZH047)the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY222034.
文摘As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.
基金College Students Innovation and Entrepreneurship Training Program(X202511049398)College Students Innovation and Entrepreneurship Training Program(X202511049201)+1 种基金College Students Innovation and Entrepreneurship Training Program(X202511258005S)University-Level Research Funding Program of Hainan Science and Technology Vocational University(HKKY2024-87)。
文摘Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.
基金National Natural Science Foundation of China,No.42230106,No.42171250State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04。
文摘Agriculture holds a pivotal position in the economic fabric of every nation,yet concerns about agricultural carbon emission intensity(ACI)have become a major hurdle to achieving global economic sustainability.Focusing on 31 provincial-level regions in China,this study uses the Exploratory Spatio-temporal Data Analysis(ESTDA)and Panel Quantile Regression(PQR)model to analyze the spatio-temporal interaction characteristics and influencing factors of ACI in China from 2004 to 2023.The findings are as follows:(1)ACI showed an overall downward trend,and the spatial distribution pattern was characterized by“high in the western region and low along the southeastern coast”.Although the overall disparity tended to converge,some high-carbon-intensity regions exhibited extreme trends.ACI displayed clear spatial directionality,with the spatial center shifting steadily toward the northeast.(2)Regions in the northwest,northeast,and central-south parts exhibited strong local spatial structural dynamics,and the local spatial dependence of ACI in each region showed a nonlinear trend.Generally speaking,the spatial association pattern demonstrated a certain degree of inertia in spatial transfer,reflecting strong path dependence or spatial lock-in characteristics.(3)Optimization of industrial structure and improvement in agricultural mechanization will increase ACI,while economic development can effectively reduce it.The impact of urbanization on ACI exhibits a nonlinear pattern.The coordinated development of economic growth and urbanization significantly reduces ACI,with a stronger emission reduction observed in regions with low ACI.The optimization of industrial structure,when combined with urbanization and environmental regulation,contributes to significant emission reductions particularly in high-ACI areas.Similarly,the synergy between agricultural mechanization and urbanization effectively lowers emissions in low-ACI regions,though this effect diminishes in areas with higher ACI.
基金supported by the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y02)Yunnan Key Laboratory of Plateau Geographic Processes and Environmental Changes,Faculty of Geography,Yunnan Normal University(PGPEC2304)China Scholarship Council。
文摘Sloping farmland,particularly in mountainous and hilly areas,constitutes a significant component of regional farmland resources.An investigation into the spatio-temporal pattern of sloping farmland and its influencing factors in China is imperative for the efficient utilization of farmland and the optimization of land space.We used land use transfer matrix,geographically weighted regression model and geographical detector to conduct this study.Results showed that sloping farmland in China firstly decreased and then increased from 2000 to 2020.The proportion of sloping farmland decreased radially outward from Sichuan basin to the surrounding areas.Change rates of sloping farmland with different slopes varied and the slope with 6°-15°underwent the fastest changes.The influencing factors of farmland at various slope degrees were different.For sloping farmland below 15°,land use intensity and elevation had the greatest contribution.For sloping farmland between 15°and 25°,elevation,land use intensity,and population density were the main influencing factors.Sloping farmland above 25°was mostly affected by natural factors.This study can provide scientific basis for rational development and protection of sloping farmland.
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
文摘Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
基金supported by The Henan Province Science and Technology Research Project(242102211046)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25A520039)+1 种基金theNatural Science Foundation project of Zhongyuan Institute of Technology(K2025YB011)the Zhongyuan University of Technology Graduate Education and Teaching Reform Research Project(JG202424).
文摘Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.
基金supported by National Natural Science Foundation of China(62462019,62172350)Guangdong Basic and Applied Basic Research Foundation(2023A1515012846)+6 种基金Guangxi Science and Technology Major Program(AA24263010)The Key Research and Development Program of Guangxi(AB24010085)Key Laboratory of Equipment Data Security and Guarantee Technology,Ministry of Education(GDZB2024060500)2024 Higher Education Scientific Research Planning Project(No.24NL0419)Nantong Science and Technology Project(No.JC2023070)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(GrantNo.SKLACSS-202407)sponsored by the Cultivation of Young andMiddle-aged Academic Leaders in the“Qing Lan Project”of Jiangsu Province and the 2025 Outstanding Teaching Team in the“Qing Lan Project”of Jiangsu Province.
文摘Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.
基金supported by the National Natural Science Foundation of China(Nos.52072059,12304078,12274061 and 11774044)the Natural Science Foundation of Sichuan Province(No.2024NSFSC1384).
文摘Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancements in the development of diverse Bi_(2)O_(2)Se heterojunctions have unveiled extensive potential applications in both electronics and optoelectronics.However,achieving an in-depth understanding of band alignment and particularly interface dynamics remains a significant challenge.In this study,we conduct a comprehensive experimental investigation into band alignment utilizing high-resolution X-ray photoelectron spectroscopy(HRXPS),while also thoroughly discussing the properties of interface states.Our findings reveal that ultrathin films of Bi_(2)O_(2)Se grown on SrTiO_(3)(with TiO_(2)(001)termination)exhibit Type-I(straddling gap)band alignment characterized by a valence band offset(VBO)of approximately 1.77±0.04 eV and a conduction band offset(CBO)around 0.68±0.04 eV.Notably,when accounting for the influence of interface states,the bands at the interface display a herringbone configuration due to substantial built-in electric fields,which markedly deviate from conventional band alignments.Thus,our results provide valuable insights for advancing high-efficiency electronic and optoelectronic devices,particularly those where charge transfer is highly sensitive to interface states.
