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
Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-graine...Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.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
Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,a...Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.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
Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.展开更多
Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil w...Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil within a strain rate range of 0.1%/min to 5.0%/min.The results showed a clear dependence of cavity pressure and excess pore pressure(EPP)on strain ratesdboth increased with higher rates for a given radial displacement.In light of the experimental results,three cases of cylindrical cavity expansion were investigated using the finite element method and analytical method,partially drained expansion in Modified Cam-Clay(MCC)soil,and undrained and partially drained expansion in elastoviscoplastic(EVP)soil.The EVP behavior was and modeled using the MCC model and the overstress viscoplastic theory.The results indicated that over the strain rate range of 0.0001%/min and 50%/min,the rate response of cavity pressure for the case of partially drained expansion in MCC soil(permeability coefficient ranging from 5×10^(-6) m/s to 2.5×10^(-11) m/s)is not obvious,while the EPP response during undrained expansion in EVP soil shows rate-independent.Only the partially drained solution for cavity expansion in EVP soil captured the rate-sensitive responses of both cavity pressure and EPP,confirmed by the pressuremeter tests on the Kunming peaty soil,Saint-Herblain clay,and Burswood clay.This suggests that the rate effect results from a combination of drainage-related and time-dependent soil behavior.Parametric studies further demonstrated that both viscous behavior and the overconsolidation ratio significantly influence cylindrical cavity expansion response,and the drainage conditions during expansion can be assessed using a nondimensional velocity.展开更多
Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robus...Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robust feature extraction and efficient fusion remain major challenges.We introduce a multi-stage fine-grained audiovisual fusion network(MSFG-AVFNet) for fine-grained bird species classification,which addresses these challenges through two key components:(1) the audiovisual feature extraction module,which adopts a multi-stage finetuning strategy to provide high-quality unimodal features,laying a solid foundation for modality fusion;(2) the audiovisual feature fusion module,which combines a max pooling aggregation strategy with a novel audiovisual loss function to achieve effective and robust feature fusion.Experiments were conducted on the self-built AVB81and the publicly available SSW60 datasets,which contain data from 81 and 60 bird species,respectively.Comprehensive experiments demonstrate that our approach achieves notable performance gains,outperforming existing state-of-the-art methods.These results highlight its effectiveness in leveraging audiovisual modalities for fine-grained bird classification and its potential to support ecological monitoring and biodiversity research.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
The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)M...The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)Mg alloys was investigated by scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),and electron backscattered diffraction(EBSD).It was found that different dislocation configurations were formed in A and B alloys.Redundant free dislocations(RFDs)and dislocation tangles were the ways to form deformation substructure in A alloy,no RFDs except dislocation tangles were found in B alloy.The interaction between nano-scale second phase particles(nano-scale C15 andβ-Mg_(17)(Al,Zn)_(12)phase)and different dislocation configurations had a significant effect on the deformation substructures formation.The mass transfer of Mg_(x)Zn_(y)Ca_(z)metastable phases and the stacking order of stacking faults were conducive to the Mg-Nd-Zn typed long period stacking ordered(LPSO)phases formation.Nano-scale C15 phases,Mg-Nd-Zn typed LPSO phases,c/a ratio,β-Mg_(17)(Al,Zn)_(12)phases were the key factors influencing the formation of textures.Different textures and grain boundary features(GB features)had a significant effect on k-value.The non-basal textures were the main factor affecting k-value in A alloy,while the high-angle grain boundary(HAGB)was the main factor affecting k-value in B alloy.展开更多
Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images...Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.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
Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existin...Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.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
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(Ma...Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(March 1),the temperature suddenly soared to 29℃,the temperature of summer,while Shandong was hit by a sudden heavy snowstorm.There are various opinions and no consensus on this inexplicable weather change.For this reason,based on the principle of the role of planets in the luminescence and heat generation of stars,the author of this article reveals the significant impact of the“Seven planets in alignment”on global climate change,and also points out that the melting of polar glaciers and the approach of the moon to the Earth is another important cause of global climate change.Therefore,countermeasures to save the abnormal changes in global climate are proposed.展开更多
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.
基金supported by National Natural Science Foundation of China(Grant Nos.42072126,42372139)the Natural Science Foundation of Sichuan Province(Grant Nos.2022NSFSC0990).
文摘Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
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 CNPC Major Science and Technology Project(2021DJ1806).
文摘Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
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.
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
基金The financial support of the National Natural Science Foundation of China(Grant Nos.41972293,42272337)the Science Fund for Distinguished Young Scholars of Hubei Province(Grant No.2023AFA078)are gratefully acknowledged.
文摘Soil responds to cavity expansion is inherently rate-dependent,especially in the case of fine-grained soils.To better understand such rate effects,self-boring pressuremeter tests were conducted on Kunming peaty soil within a strain rate range of 0.1%/min to 5.0%/min.The results showed a clear dependence of cavity pressure and excess pore pressure(EPP)on strain ratesdboth increased with higher rates for a given radial displacement.In light of the experimental results,three cases of cylindrical cavity expansion were investigated using the finite element method and analytical method,partially drained expansion in Modified Cam-Clay(MCC)soil,and undrained and partially drained expansion in elastoviscoplastic(EVP)soil.The EVP behavior was and modeled using the MCC model and the overstress viscoplastic theory.The results indicated that over the strain rate range of 0.0001%/min and 50%/min,the rate response of cavity pressure for the case of partially drained expansion in MCC soil(permeability coefficient ranging from 5×10^(-6) m/s to 2.5×10^(-11) m/s)is not obvious,while the EPP response during undrained expansion in EVP soil shows rate-independent.Only the partially drained solution for cavity expansion in EVP soil captured the rate-sensitive responses of both cavity pressure and EPP,confirmed by the pressuremeter tests on the Kunming peaty soil,Saint-Herblain clay,and Burswood clay.This suggests that the rate effect results from a combination of drainage-related and time-dependent soil behavior.Parametric studies further demonstrated that both viscous behavior and the overconsolidation ratio significantly influence cylindrical cavity expansion response,and the drainage conditions during expansion can be assessed using a nondimensional velocity.
基金supported by the Beijing Natural Science Foundation(No.5252014)the Open Fund of The Key Laboratory of Urban Ecological Environment Simulation and Protection,Ministry of Ecology and Environment of the People's Republic of China (No.UEESP-202502)the National Natural Science Foundation of China (No.62303063&32371874)。
文摘Bird monitoring and protection are essential for maintaining biodiversity,and fine-grained bird classification has become a key focus in this field.Audio-visual modalities provide critical cues for this task,but robust feature extraction and efficient fusion remain major challenges.We introduce a multi-stage fine-grained audiovisual fusion network(MSFG-AVFNet) for fine-grained bird species classification,which addresses these challenges through two key components:(1) the audiovisual feature extraction module,which adopts a multi-stage finetuning strategy to provide high-quality unimodal features,laying a solid foundation for modality fusion;(2) the audiovisual feature fusion module,which combines a max pooling aggregation strategy with a novel audiovisual loss function to achieve effective and robust feature fusion.Experiments were conducted on the self-built AVB81and the publicly available SSW60 datasets,which contain data from 81 and 60 bird species,respectively.Comprehensive experiments demonstrate that our approach achieves notable performance gains,outperforming existing state-of-the-art methods.These results highlight its effectiveness in leveraging audiovisual modalities for fine-grained bird classification and its potential to support ecological monitoring and biodiversity research.
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financial support by the National Natural Science Foundation of China(No.51364032)the Inner Mongolia Natural Science Foundation(No.2022MS05028)。
文摘The spray-deposition was used to produce billets of Mg-4Al-1.5Zn-3Ca-1Nd(A alloy)and Mg-13Al-3Zn-3Ca-1Nd(B alloy),and evolution of deformation substructure and Mg_(x)Zn_(y)Ca_(z)metastable phase in fine-grained(3μm)Mg alloys was investigated by scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),and electron backscattered diffraction(EBSD).It was found that different dislocation configurations were formed in A and B alloys.Redundant free dislocations(RFDs)and dislocation tangles were the ways to form deformation substructure in A alloy,no RFDs except dislocation tangles were found in B alloy.The interaction between nano-scale second phase particles(nano-scale C15 andβ-Mg_(17)(Al,Zn)_(12)phase)and different dislocation configurations had a significant effect on the deformation substructures formation.The mass transfer of Mg_(x)Zn_(y)Ca_(z)metastable phases and the stacking order of stacking faults were conducive to the Mg-Nd-Zn typed long period stacking ordered(LPSO)phases formation.Nano-scale C15 phases,Mg-Nd-Zn typed LPSO phases,c/a ratio,β-Mg_(17)(Al,Zn)_(12)phases were the key factors influencing the formation of textures.Different textures and grain boundary features(GB features)had a significant effect on k-value.The non-basal textures were the main factor affecting k-value in A alloy,while the high-angle grain boundary(HAGB)was the main factor affecting k-value in B alloy.
基金supported by National Natural Science Foundation of China(No.62471034)Hebei Natural Science Foundation(No.F2023105001).
文摘Fine-grained aircraft target detection in remote sensing holds significant research valueand practical applications,particularly in military defense and precision strikes.Given the complex-ity of remote sensing images,where targets are often small and similar within categories,detectingthese fine-grained targets is challenging.To address this,we constructed a fine-grained dataset ofremotely sensed airplanes;for the problems of remote sensing fine-grained targets with obvious head-to-tail distributions and large variations in target sizes,we proposed the DWDet fine-grained tar-get detection and recognition algorithm.First,for the problem of unbalanced category distribution,we adopt an adaptive sampling strategy.In addition,we construct a deformable convolutional blockand improve the decoupling head structure to improve the detection effect of the model ondeformed targets.Then,we design a localization loss function,which is used to improve the model’slocalization ability for targets of different scales.The experimental results show that our algorithmimproves the overall accuracy of the model by 4.1%compared to the baseline model,and improvesthe detection accuracy of small targets by 12.2%.The ablation and comparison experiments alsoprove the effectiveness of our algorithm.
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 the National Natural Science Foundation of China,China (Grants No.62171232)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘Fine-grained Image Recognition(FGIR)task is dedicated to distinguishing similar sub-categories that belong to the same super-category,such as bird species and car types.In order to highlight visual differences,existing FGIR works often follow two steps:discriminative sub-region localization and local feature representation.However,these works pay less attention on global context information.They neglect a fact that the subtle visual difference in challenging scenarios can be highlighted through exploiting the spatial relationship among different subregions from a global view point.Therefore,in this paper,we consider both global and local information for FGIR,and propose a collaborative teacher-student strategy to reinforce and unity the two types of information.Our framework is implemented mainly by convolutional neural network,referred to Teacher-Student Based Attention Convolutional Neural Network(T-S-ACNN).For fine-grained local information,we choose the classic Multi-Attention Network(MA-Net)as our baseline,and propose a type of boundary constraint to further reduce background noises in the local attention maps.In this way,the discriminative sub-regions tend to appear in the area occupied by fine-grained objects,leading to more accurate sub-region localization.For fine-grained global information,we design a graph convolution based Global Attention Network(GA-Net),which can combine extracted local attention maps from MA-Net with non-local techniques to explore spatial relationship among subregions.At last,we develop a collaborative teacher-student strategy to adaptively determine the attended roles and optimization modes,so as to enhance the cooperative reinforcement of MA-Net and GA-Net.Extensive experiments on CUB-200-2011,Stanford Cars and FGVC Aircraft datasets illustrate the promising performance of our framework.
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.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
文摘Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(March 1),the temperature suddenly soared to 29℃,the temperature of summer,while Shandong was hit by a sudden heavy snowstorm.There are various opinions and no consensus on this inexplicable weather change.For this reason,based on the principle of the role of planets in the luminescence and heat generation of stars,the author of this article reveals the significant impact of the“Seven planets in alignment”on global climate change,and also points out that the melting of polar glaciers and the approach of the moon to the Earth is another important cause of global climate change.Therefore,countermeasures to save the abnormal changes in global climate are proposed.
