As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent si...Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent signals.In this paper,we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them,respectively.Based on this information,we propose a new method to achieve adaptive cross-term removal by combining seeded region growing.Compared to other methods,the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term.Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals.And it performs well in low signal-to-noise ratio environments.展开更多
The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.How...The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.展开更多
The demand for flexible electric heating devices has increased due to technology advancement and improved living standards.These devices have various applications including personal thermal management,hyperthermia,def...The demand for flexible electric heating devices has increased due to technology advancement and improved living standards.These devices have various applications including personal thermal management,hyperthermia,defrosting,agricultural heating film,and oil-water separation.Joule heat,generated by electric currents,is commonly used in electrical appliances.To incorporate Joule heating into flexible electronics,new materials with excellent mechanical properties are necessary.Traditional polymers,used as reinforcements,limit the continuity of conductive networks in composites.Therefore,there is a need to develop flexible Joule thermal composite materials with enhanced mechanical strength and conductivity.Cellulose,a widely available renewable resource,is attracting attention for its excellent mechanical properties.It can be used as a dispersant and reinforcing agent for conductive fillers in cellulose-based composites,creating highly conductive networks.Various forms of cellulose,such as wood,nanocellulose,pulp fiber,bacterial cellulose,cellulose paper,textile clothing,and aramid fiber,have been utilized to achieve high-performance Joule thermal composites.Researchers have achieved excellent mechanical properties and developed efficient electric heating devices by designing cellulose-based composites with different structures.The scalable production methods enable large-scale application of cellulose-based devices,each with unique advantages in 1D,2D,and 3D structures.This review summarizes recent advancements in cellulose-based Joule thermal composites,providing insights into different structural devices,and discussing prospects and challenges in the field.展开更多
Objectives:Schizophrenia is a profoundly stigmatized mental health condition,characterized by misconceptions that affect societal attitudes,policy development,and the lived experiences of individuals with the conditio...Objectives:Schizophrenia is a profoundly stigmatized mental health condition,characterized by misconceptions that affect societal attitudes,policy development,and the lived experiences of individuals with the condition.This study aimed to develop and validate a multidimensional scale for assessing societal stigma towards schizophrenia,while exploring how demographic factors influence such attitudes.Methods:Drawing on an extensive literature review and consultations,the study identified five domains of stigma:Workplace Capability,Intimate Relationships,Autonomy,Risk Perception,and Recovery.Using a two-phase methodology,a preliminary 38-itemscale was administered to 729 participants from the general Spanish population,refining the measure through descriptive and exploratory factor analysis.Subsequently,a revised 34-item scale was validated through confirmatory factor analysis with an independent sample of 417 participants.Results:The final model showed good fit(RMSEA=0.056,CFI=0.938,TLI=0.933)and strong internal consistency(α=0.73–0.86).Findings revealed that stigma was most pronounced in the domain of Autonomy(Mean=2.83,SD=0.91),reflecting pervasive doubts about individuals’ability to live independently and achieve meaningful integration into society.Stigma varied significantly across demographic variables,with higher levels reported among men,older individuals,married participants,and those outside health professions(p<0.01).Conversely,healthcare professionals,younger individuals,and those familiar with someone with schizophrenia generally reported less stigma(p<0.01).Conclusion:This study developed and validated a robust multidimensional scale for assessing societal stigma toward schizophrenia.The five-factor model—Workplace Capability,Intimate Relationships,Autonomy,Risk Perception,and Recovery—was empirically supported.Autonomy and Recovery emerged as themost stigmatized domains across the Spanish general population.The scale demonstrated strong psychometric properties and effectively captured stigma patterns linked to key sociodemographic variables.展开更多
Global framework for nature management requires identifying areas of high priority for biodiversity conservation and restoration.The unique environments of Qinghai-Xizang Plateau(QXP) provide irreplaceable habitats fo...Global framework for nature management requires identifying areas of high priority for biodiversity conservation and restoration.The unique environments of Qinghai-Xizang Plateau(QXP) provide irreplaceable habitats for biodiversity which is prominent under future climate change.Despite the recent increase in research interest on conservation priorities,there is a lack of comprehensive and targeted protection strategies for pandemic species under climate change.Here,we compiled an exhaustive dataset with the variables of extinction risk and occurrence records of ectothermic lizards to investigate the conservation priorities in the QXP.We assessed the conservation status of the QXP lizards and identified the priority protected areas within the QXP under future climate scenarios using phylogenetic generalized least squares and ensemble species distribution models.Our analyses suggested nine lizard species to be prioritized for protection,with the most critical priority species being Dopasia gracilis,D.harti,and Phrynocephalus putjatai.Moreover,the priority protected areas covered~4.7%area of the QXP,mostly in the southern QXP and southeastern Hengduan Mountains.Protecting these regions would achieve a conservation effectiveness of≥95%for species richness,phylogenetic and functionaldiversity under climate change.Our findings provide realistic guidance for improving the conservation effectiveness of specific-lizard and-regions in the QXP under climate change.Our“bottom-up”approach could help the conservation efforts of other regions and species as an alternative to“top-down”global maps.展开更多
The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distributio...The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distribution patterns and dynamics for botanists,ecologists,conservation biologists,and biogeographers.This study proposes a gridded vector data integration method,combining grid-based techniques with vectorization to integrate diverse data types from multiple sources into grids of the same scale.Here we demonstrate the methodology by creating a comprehensive 1°×1°database of western China that includes plant distribution information and environmental factor data.This approach addresses the need for a standardized data system to facilitate exploration of plant distribution patterns and dynamic changes in the region.展开更多
Indian Railways have been the largest people moving transport infrastructure in India.Over the years the systems and trains have been upgraded resulting in both better passenger amenities and reduction in travel time....Indian Railways have been the largest people moving transport infrastructure in India.Over the years the systems and trains have been upgraded resulting in both better passenger amenities and reduction in travel time.The newest addition is the Vande Bharat Express,a semi-high-speed train that was introduced in India in 2019.The train currently runs between 10 routes and has brought significant changes to India’s railway network.This article explores the introduction of Vande Bharat Express trains in India and its effects on the country’s interstation time-space shrinkage using cartographic techniques.The cartographic techniques like stepwise multidimensional scaling and interpolation using the distance cartogram plugin in QGIS are mainly used for generating the time-space maps for various speeds.The limitations of these techniques and the methods to overcome those limitations are also explored in this article.展开更多
Confronting the escalating global challenge of counterfeit products,developing advanced anticounterfeiting materials and structures with physical unclonable functions(PUFs)has become imperative.All-optical PUFs,distin...Confronting the escalating global challenge of counterfeit products,developing advanced anticounterfeiting materials and structures with physical unclonable functions(PUFs)has become imperative.All-optical PUFs,distinguished by their high output complexity and expansive response space,offer a promising alternative to conventional electronic counterparts.For practical authentications,the expansion of optical PUF keys usually involves intricate spatial or spectral shaping of excitation light using bulky external apparatus,which largely hinders the applications of optical PUFs.Here,we report a plasmonic PUF system based on heterogeneous nanostructures.The template-assisted shadow deposition technique was employed to adjust the morphological diversity of densely packed metal nanoparticles in individual PUFs.Transmission images were processed via a hash algorithm,and the generated PUF keys with a scalable capacity from 2875 to 243401 exhibit excellent uniqueness,randomness,and reproducibility.Furthermore,the wavelength and the polarization state of the excitation light are harnessed as two distinct expanding strategies,offering the potential for multiscenario applications via a single PUF.Overall,our reported plasmonic PUFs operated with the multidimensional expanding strategy are envisaged to serve as easy-to-integrate,easy-to-use systems and promise efficacy across a broad spectrum of applications,from anticounterfeiting to data encryption and authentication.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
Ink-jetting printing stands out among various conformal additive manufacturing techniques for its multi-material,digital control,and process flexibility.Ink-jetting-based conformal additive manufacturing is renowned f...Ink-jetting printing stands out among various conformal additive manufacturing techniques for its multi-material,digital control,and process flexibility.Ink-jetting-based conformal additive manufacturing is renowned for its adaptability to complex topological surfaces and is emerging as a critical technology for future comprehensive conformal printing systems.This review highlights the distinctiveness of four primary ink-jetting printing techniques in conformal additive manufacturing—piezoelectric jetting,thermal bubble jetting,aerosol jetting,and electrohydrodynamic jetting—and delves into how these attributes endow ink-jetting printing with unique advantages in conformal processes.Furthermore,leveraging these advantages,the review discusses potential applications in conformal electronics,energy devices,biology,and electromagnetics to bolster the ongoing development and application.Considering the current state of this technology,the review identifies critical challenges for future advancements,such as dynamic surface printing,integrated fabrication of multifunctional conformal structures,and the balance between resolution and throughput.This review summarizes the latest research and technological advancements in ink-jetting-based conformal additive manufacturing,aiding in its innovative applications and enhanced manufacturing capabilities in the future.展开更多
The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first dis...The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.展开更多
Multidimensional confined structure systems are proposed and demonstrated by using MoO_(2)@MO_(2)C(MMC)to enhance the photothermal catalytic performance of the metal sulfides-multidimensional confined structure(TMs-MD...Multidimensional confined structure systems are proposed and demonstrated by using MoO_(2)@MO_(2)C(MMC)to enhance the photothermal catalytic performance of the metal sulfides-multidimensional confined structure(TMs-MDCS).Specifically,the MMC nanoparticles confined to the surface of the ZnIn_(2)S_(4)hollow tube-shell(MMC/HT-ZIS)achieve a hydrogen evolution rate of 9.72 mmol g^(-1)h^(-1),which is 11.2 times higher than that of pure HT-ZIS.Meanwhile,the MnCdS(MCS)nanoparticles are encapsulated within the two-dimensional MMC(2D MMC/MCS)through precise regulation of size and morphology.The 10-MMC/MCS lamellar network demonstrates the highest hydrogen evolution rate of 8.19 mmol g^(-1)-h^(-1).The obtained MMC/TMs-MDCS catalysts exhibit an enhanced photocatalytic hydrogen evolution rate,which can be attributed to the strong synergistic interaction between the multidimensional confinement and the photothermal effects.The confinement space and the strong interfacial relationship within the MMC/TMs-MDCS create abundant channels and active sites that facilitate electron migration and transport.Furthermore,the construction of a confined environment positions these materials as promising candidates for achieving exceptional photothermal catalytic performance,as MMC/TMs-MDCS enhance light absorption through light scattering and reflecting effects.Additionally,the capacity of MMC/TMsMDCS to convert solar light into thermal energy significantly reduces the activation energy of the reaction,thereby facilitating reaction kinetics and accelerating the separation and transport of photogenerated carriers.This work provides valuable insights for the development of highly efficient photothermal catalytic water-splitting systems for hydrogen production using multidimensional confined catalysts.展开更多
An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra...An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.展开更多
This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource ...This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource node(RN)instead of rotating a mother constellation(MC)as in the conventional SCMA works.In the other one,based on the MC,a multi-resources rotated codebooks are proposed to improve the performance of the superimposed constellations.The resultant codebooks are respectively referred to as the resource edge multidimensional codebooks(REMC)and the user edge multi-dimensional codebooks(UEMC).Additionally,we delve into the detailed design of the MC and the superimposed constellation.Then,we pay special attention to the application of the proposed schemes to challenging design cases,particularly for the high dimensional,high rate,and irregular codebooks,where the corresponding simplified schemes are proposed to reduce the complexity of codebook design.Finally,simulation results are presented to demonstrate the superiority of our progressive edge growth-based schemes.The numerical results indicate that the proposed codebooks significantly outperform the stateof-the-art codebooks.In addition,we also show that the proposed REMC codebooks outperform in the lower signal-to-noise ratio(SNR)regime,whereas the UEMC codebooks exhibit better performance at higher SNRs.展开更多
Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery fa...Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
The hollow porous structure with exceptional interfacial effect and customizable internal environment shows significant potential for application as electromagnetic shielding and absorption materials.However,designing...The hollow porous structure with exceptional interfacial effect and customizable internal environment shows significant potential for application as electromagnetic shielding and absorption materials.However,designing hollow porous electromagnetic absorbers with both desirable impedance matching and high loss capability remains a challenge.Herein,3D hollow porous electromagnetic microspheres were constructed by assembling 0D Co magnetic nanoparticles,1D carbon nanotubes,and 2D carbon nanosheets.Due to the sufficient sites for Co^(2+)riveting,the high loading of magnetic carbon nanotubes(CoNC)and porous carbon spheres formed high-density interfaces,enhancing the interfacial polarization.Furthermore,high-density CoNC were grown in situ on the hollow porous carbon(HPC)microsphere,forming a highly dispersed 3D magnetic network that inhibited the aggregation of magnetic nanoparticles and enhanced magnetic coupling.Therefore,the asprepared CoNC/HPC microspheres exhibited excellent microwave absorption(MA)performance,with a minimum reflection loss of-33.2 dB and an effective bandwidth of 5.5 GHz at a thickness of only 1.8 mm.The interfacial polarization mechanism for enhanced MA performance was demonstrated by electron holography and density functional theory calculations.Magnetic holography and micromagnetic simulations also revealed magnetic confinement and coupling mechanism.This work provides a new approach for designing electromagnetic absorbers with optimized impedance matching and loss capability.展开更多
Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In exist...Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.展开更多
This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contra...This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.展开更多
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金Supported by the National Natural Science Foundation of China(62201171).
文摘Wigner-Ville distribution(WVD)is widely used in the field of signal processing due to its excellent time-frequency(TF)concentration.However,WVD is severely limited by the cross-term when working with multicomponent signals.In this paper,we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them,respectively.Based on this information,we propose a new method to achieve adaptive cross-term removal by combining seeded region growing.Compared to other methods,the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term.Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals.And it performs well in low signal-to-noise ratio environments.
基金Supported by the National Key Research and Development Program of China(2022YFA1404602)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0580000)+3 种基金the National Natural Science Foundation of China(U23B2045,62305362)the Program of Shanghai Academic/Technology Research Leader(22XD1424400)the Fund of SITP Innovation Foundation(CX-461 and CX-522)Special Project to Seize the Commanding Heights of Science and Technology of Chinese Academy of Sciences,subtopic(GJ0090406-6).
文摘The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.
基金supported by the fund of the National Natural Science Foundation of China(Nos.22378249,22078184,and 22171170)the China Postdoctoral Science Foundation(No.2019M653853XB)the Natural Science Advance Research Foundation of Shaanxi University of Science and Technology(No.2018QNBJ-03).
文摘The demand for flexible electric heating devices has increased due to technology advancement and improved living standards.These devices have various applications including personal thermal management,hyperthermia,defrosting,agricultural heating film,and oil-water separation.Joule heat,generated by electric currents,is commonly used in electrical appliances.To incorporate Joule heating into flexible electronics,new materials with excellent mechanical properties are necessary.Traditional polymers,used as reinforcements,limit the continuity of conductive networks in composites.Therefore,there is a need to develop flexible Joule thermal composite materials with enhanced mechanical strength and conductivity.Cellulose,a widely available renewable resource,is attracting attention for its excellent mechanical properties.It can be used as a dispersant and reinforcing agent for conductive fillers in cellulose-based composites,creating highly conductive networks.Various forms of cellulose,such as wood,nanocellulose,pulp fiber,bacterial cellulose,cellulose paper,textile clothing,and aramid fiber,have been utilized to achieve high-performance Joule thermal composites.Researchers have achieved excellent mechanical properties and developed efficient electric heating devices by designing cellulose-based composites with different structures.The scalable production methods enable large-scale application of cellulose-based devices,each with unique advantages in 1D,2D,and 3D structures.This review summarizes recent advancements in cellulose-based Joule thermal composites,providing insights into different structural devices,and discussing prospects and challenges in the field.
文摘Objectives:Schizophrenia is a profoundly stigmatized mental health condition,characterized by misconceptions that affect societal attitudes,policy development,and the lived experiences of individuals with the condition.This study aimed to develop and validate a multidimensional scale for assessing societal stigma towards schizophrenia,while exploring how demographic factors influence such attitudes.Methods:Drawing on an extensive literature review and consultations,the study identified five domains of stigma:Workplace Capability,Intimate Relationships,Autonomy,Risk Perception,and Recovery.Using a two-phase methodology,a preliminary 38-itemscale was administered to 729 participants from the general Spanish population,refining the measure through descriptive and exploratory factor analysis.Subsequently,a revised 34-item scale was validated through confirmatory factor analysis with an independent sample of 417 participants.Results:The final model showed good fit(RMSEA=0.056,CFI=0.938,TLI=0.933)and strong internal consistency(α=0.73–0.86).Findings revealed that stigma was most pronounced in the domain of Autonomy(Mean=2.83,SD=0.91),reflecting pervasive doubts about individuals’ability to live independently and achieve meaningful integration into society.Stigma varied significantly across demographic variables,with higher levels reported among men,older individuals,married participants,and those outside health professions(p<0.01).Conversely,healthcare professionals,younger individuals,and those familiar with someone with schizophrenia generally reported less stigma(p<0.01).Conclusion:This study developed and validated a robust multidimensional scale for assessing societal stigma toward schizophrenia.The five-factor model—Workplace Capability,Intimate Relationships,Autonomy,Risk Perception,and Recovery—was empirically supported.Autonomy and Recovery emerged as themost stigmatized domains across the Spanish general population.The scale demonstrated strong psychometric properties and effectively captured stigma patterns linked to key sociodemographic variables.
基金supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20050201)the National Natural Science Foundation of China (31861143023,31872250 and 31872252)。
文摘Global framework for nature management requires identifying areas of high priority for biodiversity conservation and restoration.The unique environments of Qinghai-Xizang Plateau(QXP) provide irreplaceable habitats for biodiversity which is prominent under future climate change.Despite the recent increase in research interest on conservation priorities,there is a lack of comprehensive and targeted protection strategies for pandemic species under climate change.Here,we compiled an exhaustive dataset with the variables of extinction risk and occurrence records of ectothermic lizards to investigate the conservation priorities in the QXP.We assessed the conservation status of the QXP lizards and identified the priority protected areas within the QXP under future climate scenarios using phylogenetic generalized least squares and ensemble species distribution models.Our analyses suggested nine lizard species to be prioritized for protection,with the most critical priority species being Dopasia gracilis,D.harti,and Phrynocephalus putjatai.Moreover,the priority protected areas covered~4.7%area of the QXP,mostly in the southern QXP and southeastern Hengduan Mountains.Protecting these regions would achieve a conservation effectiveness of≥95%for species richness,phylogenetic and functionaldiversity under climate change.Our findings provide realistic guidance for improving the conservation effectiveness of specific-lizard and-regions in the QXP under climate change.Our“bottom-up”approach could help the conservation efforts of other regions and species as an alternative to“top-down”global maps.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0502)the National Natural Science Foundation of China(32322006)+1 种基金the Major Program for Basic Research Project of Yunnan Province(202103AF140005 and 202101BC070002)the Practice Innovation Fund for Professional Degree Graduates of Yunnan University(ZC-22222401).
文摘The study of plant diversity is often hindered by the challenge of integrating data from different sources and different data types.A standardized data system would facilitate detailed exploration of plant distribution patterns and dynamics for botanists,ecologists,conservation biologists,and biogeographers.This study proposes a gridded vector data integration method,combining grid-based techniques with vectorization to integrate diverse data types from multiple sources into grids of the same scale.Here we demonstrate the methodology by creating a comprehensive 1°×1°database of western China that includes plant distribution information and environmental factor data.This approach addresses the need for a standardized data system to facilitate exploration of plant distribution patterns and dynamic changes in the region.
文摘Indian Railways have been the largest people moving transport infrastructure in India.Over the years the systems and trains have been upgraded resulting in both better passenger amenities and reduction in travel time.The newest addition is the Vande Bharat Express,a semi-high-speed train that was introduced in India in 2019.The train currently runs between 10 routes and has brought significant changes to India’s railway network.This article explores the introduction of Vande Bharat Express trains in India and its effects on the country’s interstation time-space shrinkage using cartographic techniques.The cartographic techniques like stepwise multidimensional scaling and interpolation using the distance cartogram plugin in QGIS are mainly used for generating the time-space maps for various speeds.The limitations of these techniques and the methods to overcome those limitations are also explored in this article.
基金supported by the National Natural Science Foundation of China(Grant Nos.62422503,62105080,22004016,and U22A2093)the Guangdong Basic and Applied Basic Research Foundation Regional Joint Fund(Grant Nos.2023A1515011944,2020B1515130006,and 2021B515120056)+1 种基金the Talent Recruitment Project of Guangdong(Grant No.2021QN02X179)the Science and Technology Innovation Commission of Shenzhen(Grant Nos.JCYJ20220531095604009 and RCYX20221008092907027).
文摘Confronting the escalating global challenge of counterfeit products,developing advanced anticounterfeiting materials and structures with physical unclonable functions(PUFs)has become imperative.All-optical PUFs,distinguished by their high output complexity and expansive response space,offer a promising alternative to conventional electronic counterparts.For practical authentications,the expansion of optical PUF keys usually involves intricate spatial or spectral shaping of excitation light using bulky external apparatus,which largely hinders the applications of optical PUFs.Here,we report a plasmonic PUF system based on heterogeneous nanostructures.The template-assisted shadow deposition technique was employed to adjust the morphological diversity of densely packed metal nanoparticles in individual PUFs.Transmission images were processed via a hash algorithm,and the generated PUF keys with a scalable capacity from 2875 to 243401 exhibit excellent uniqueness,randomness,and reproducibility.Furthermore,the wavelength and the polarization state of the excitation light are harnessed as two distinct expanding strategies,offering the potential for multiscenario applications via a single PUF.Overall,our reported plasmonic PUFs operated with the multidimensional expanding strategy are envisaged to serve as easy-to-integrate,easy-to-use systems and promise efficacy across a broad spectrum of applications,from anticounterfeiting to data encryption and authentication.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
基金supported by the National Natural Science Foundation of China(Grant Nos.52005059 and 52375306)the Open Foundation of the Key Laboratory of Advanced Marine Materials(Grant No.2024K01)the Graduate Research and Innovation Foundation of Chongqing,China(Grant No.CYB240011)。
文摘Ink-jetting printing stands out among various conformal additive manufacturing techniques for its multi-material,digital control,and process flexibility.Ink-jetting-based conformal additive manufacturing is renowned for its adaptability to complex topological surfaces and is emerging as a critical technology for future comprehensive conformal printing systems.This review highlights the distinctiveness of four primary ink-jetting printing techniques in conformal additive manufacturing—piezoelectric jetting,thermal bubble jetting,aerosol jetting,and electrohydrodynamic jetting—and delves into how these attributes endow ink-jetting printing with unique advantages in conformal processes.Furthermore,leveraging these advantages,the review discusses potential applications in conformal electronics,energy devices,biology,and electromagnetics to bolster the ongoing development and application.Considering the current state of this technology,the review identifies critical challenges for future advancements,such as dynamic surface printing,integrated fabrication of multifunctional conformal structures,and the balance between resolution and throughput.This review summarizes the latest research and technological advancements in ink-jetting-based conformal additive manufacturing,aiding in its innovative applications and enhanced manufacturing capabilities in the future.
基金Supported by the National Natural Science Foundation of China(12271154)the Natural Science Foundation of Hunan Province(2022JJ30234)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20231032)。
文摘The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.
基金supported by the Postgraduate Education Reform Project of Shandong Province(SDYAL2023032)the National Key Research and Development Program(2021YFB3500102)。
文摘Multidimensional confined structure systems are proposed and demonstrated by using MoO_(2)@MO_(2)C(MMC)to enhance the photothermal catalytic performance of the metal sulfides-multidimensional confined structure(TMs-MDCS).Specifically,the MMC nanoparticles confined to the surface of the ZnIn_(2)S_(4)hollow tube-shell(MMC/HT-ZIS)achieve a hydrogen evolution rate of 9.72 mmol g^(-1)h^(-1),which is 11.2 times higher than that of pure HT-ZIS.Meanwhile,the MnCdS(MCS)nanoparticles are encapsulated within the two-dimensional MMC(2D MMC/MCS)through precise regulation of size and morphology.The 10-MMC/MCS lamellar network demonstrates the highest hydrogen evolution rate of 8.19 mmol g^(-1)-h^(-1).The obtained MMC/TMs-MDCS catalysts exhibit an enhanced photocatalytic hydrogen evolution rate,which can be attributed to the strong synergistic interaction between the multidimensional confinement and the photothermal effects.The confinement space and the strong interfacial relationship within the MMC/TMs-MDCS create abundant channels and active sites that facilitate electron migration and transport.Furthermore,the construction of a confined environment positions these materials as promising candidates for achieving exceptional photothermal catalytic performance,as MMC/TMs-MDCS enhance light absorption through light scattering and reflecting effects.Additionally,the capacity of MMC/TMsMDCS to convert solar light into thermal energy significantly reduces the activation energy of the reaction,thereby facilitating reaction kinetics and accelerating the separation and transport of photogenerated carriers.This work provides valuable insights for the development of highly efficient photothermal catalytic water-splitting systems for hydrogen production using multidimensional confined catalysts.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272259 and 52005148).
文摘An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.
文摘This paper proposes a class of novel progressive edge growth-based codebooks for downlink sparse code multiple access(SCMA)systems.In the first scheme,we propose to progressively design the codebooks of each resource node(RN)instead of rotating a mother constellation(MC)as in the conventional SCMA works.In the other one,based on the MC,a multi-resources rotated codebooks are proposed to improve the performance of the superimposed constellations.The resultant codebooks are respectively referred to as the resource edge multidimensional codebooks(REMC)and the user edge multi-dimensional codebooks(UEMC).Additionally,we delve into the detailed design of the MC and the superimposed constellation.Then,we pay special attention to the application of the proposed schemes to challenging design cases,particularly for the high dimensional,high rate,and irregular codebooks,where the corresponding simplified schemes are proposed to reduce the complexity of codebook design.Finally,simulation results are presented to demonstrate the superiority of our progressive edge growth-based schemes.The numerical results indicate that the proposed codebooks significantly outperform the stateof-the-art codebooks.In addition,we also show that the proposed REMC codebooks outperform in the lower signal-to-noise ratio(SNR)regime,whereas the UEMC codebooks exhibit better performance at higher SNRs.
基金supported by the National Key R&D Program of China(2022YFB2404300)the National Natural Science Foundation of China(NSFC Nos.52177217 and 52106244)。
文摘Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金supported by the National Natural Science Foundation of China(Nos.52231007,51725101,11727807)the Ministry of Science and Technology of China(Nos.2021YFA1200600 and 2018YFA0209102).
文摘The hollow porous structure with exceptional interfacial effect and customizable internal environment shows significant potential for application as electromagnetic shielding and absorption materials.However,designing hollow porous electromagnetic absorbers with both desirable impedance matching and high loss capability remains a challenge.Herein,3D hollow porous electromagnetic microspheres were constructed by assembling 0D Co magnetic nanoparticles,1D carbon nanotubes,and 2D carbon nanosheets.Due to the sufficient sites for Co^(2+)riveting,the high loading of magnetic carbon nanotubes(CoNC)and porous carbon spheres formed high-density interfaces,enhancing the interfacial polarization.Furthermore,high-density CoNC were grown in situ on the hollow porous carbon(HPC)microsphere,forming a highly dispersed 3D magnetic network that inhibited the aggregation of magnetic nanoparticles and enhanced magnetic coupling.Therefore,the asprepared CoNC/HPC microspheres exhibited excellent microwave absorption(MA)performance,with a minimum reflection loss of-33.2 dB and an effective bandwidth of 5.5 GHz at a thickness of only 1.8 mm.The interfacial polarization mechanism for enhanced MA performance was demonstrated by electron holography and density functional theory calculations.Magnetic holography and micromagnetic simulations also revealed magnetic confinement and coupling mechanism.This work provides a new approach for designing electromagnetic absorbers with optimized impedance matching and loss capability.
基金supported by National Natural Sciences Foundation of China(No.62271165,62027802,62201307)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030297)+2 种基金the Shenzhen Science and Technology Program ZDSYS20210623091808025Stable Support Plan Program GXWD20231129102638002the Major Key Project of PCL(No.PCL2024A01)。
文摘Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.
基金supported by the Agence Nationale de la Recherche(ANR)(contract“ANR-17-EURE-0002”)by the Region of Bourgogne Franche-ComtéCADRAN Projectsupported by the European Research Council(ERC)project HYPATIA under the European Union's Horizon 2020 research and innovation programme.Grant agreement n.835294。
文摘This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.
