Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and h...Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.展开更多
As a popular approach to producing atmospheric pressure non-thermal plasma,dielectric barrier discharge(DBD)has been extensively used in various application fields.In this paper,DBD with wavy dielectric layers is nume...As a popular approach to producing atmospheric pressure non-thermal plasma,dielectric barrier discharge(DBD)has been extensively used in various application fields.In this paper,DBD with wavy dielectric layers is numerically simulated in atmospheric pressure helium mixed with trace nitrogen based on a fluid model.With varying relative position(phase difference(Δφ))of the wavy surfaces,there is a positive discharge and a negative discharge per voltage cycle,each of which consists of a pulse stage and a hump stage.For the pulse stage,maximal current increases with increasingΔφ.Results show that DBD with the wavy surfaces appears as discrete micro-discharges(MDs),which are self-organized to different patterns with varyingΔφ.The MDs are vertical and uniformly-spaced withΔφ=0,which are self-organized in pairs withΔφ=π/4.These MD pairs are merged into some bright wide MDs withΔφ=π/2.In addition,narrow MDs appear between tilted wide MDs withΔφ=3π/4.WithΔφ=π,the pattern is composed of wide and narrow MDs,which are vertical and appear alternately.To elucidate the formation mechanism of the patterns with differentΔφ,temporal evolutions of electron density and electric field are investigated for the positive discharge.Moreover,surface charge on the wavy dielectric layers has also been compared with differentΔφ.展开更多
Skeletal muscle has a robust regeneration ability that is impaired by severe injury,disease,and aging.resulting in a decline in skeletal muscle function.Therefore,improving skeletal muscle regeneration is a key challe...Skeletal muscle has a robust regeneration ability that is impaired by severe injury,disease,and aging.resulting in a decline in skeletal muscle function.Therefore,improving skeletal muscle regeneration is a key challenge in treating skeletal muscle-related disorders.Owing to their significant role in tissue regeneration,implantation of M2 macrophages(M2MФ)has great potential for improving skeletal muscle regeneration.Here,we present a short-wave infrared(SWIR)fluorescence imaging technique to obtain more in vivo information for an in-depth evaluation of the skeletal muscle regeneration effect after M2MФtransplantation.SWIR fluorescence imaging was employed to track implanted M2MФin the injured skeletal muscle of mouse models.It is found that the implanted M2MФaccumulated at the injury site for two weeks.Then,SWIR fluorescence imaging of blood vessels showed that M2MФimplantation could improve the relative perfusion ratio on day 5(1.09±0.09 vs 0.85±0.05;p=0.01)and day 9(1.38±0.16 vs 0.95±0.03;p=0.01)post-injury,as well as augment the degree of skeletal muscle regencration on day 13 post-injury.Finally,multiple linear regression analyses determined that post-injury time and relative perfusion ratio could be used as predictive indicators to evaluate skeletal muscle regeneration.These results provide more in vivo details about M2MФin skeletal muscle regeneration and confirm that M2MФcould promote angiogenesis and improve the degree of skeletal muscle repair,which will guide the research and development of M2MФimplantation to improve skeletal muscle regeneration.展开更多
Mutations in long-chain acyl-CoA synthetase 4 (ACSL4) are associated with non-syndromic X-linked intellectual disability (ID). However, the neural functions of ACSL4 and how loss of ACSL4 leads to ID remain largely un...Mutations in long-chain acyl-CoA synthetase 4 (ACSL4) are associated with non-syndromic X-linked intellectual disability (ID). However, the neural functions of ACSL4 and how loss of ACSL4 leads to ID remain largely unexplored. We report here that mutations in Acsl, the Drosophila ortholog of human ACSL3 and ACSL4, result in developmental defects of the mushroom body (MB), the center of olfactory learning and memory. Specifically, Acsl mutants show fewer MB neuroblasts (Nbs) due to reduced proliferation activity and premature differentiation. Consistently, these surviving Nbs show reduced expression of cyclin E, a key regulator of the G1-to S-phase cell cycle transition, and nuclear mislocalization of the transcriptional factor Prospero, which is known to repress self-renewal genes and activate differentiating genes. Furthermore, RNA-seq analysis reveals downregulated Nb-and cell-cyclerelated genes and upregulated neuronal differentiation genes in Acsl mutant Nbs. As Drosophila Acsl and human ACSL4 are functionally conserved, our findings provide novel insights into a critical and previously unappreciated role of Acsl in neurogenesis and the pathogenesis of ACSL4-related ID.展开更多
AIM: To explore hemodynamics and vasoactive substance levels during renal vein congestion that occurs in the anhepatic phase of liver transplantation.METHODS: New Zealand rabbits received ligation of the hepatic pedic...AIM: To explore hemodynamics and vasoactive substance levels during renal vein congestion that occurs in the anhepatic phase of liver transplantation.METHODS: New Zealand rabbits received ligation of the hepatic pedicle, supra-hepatic vena cava and infrahepatic vena cava [anhepatic phase group(APH); n = 8], the renal veins(RVL; n = 8), renal veins and hepatic pedicle [with the inferior vena cava left open)(RVHP; n = 8)], or a sham operation(SOP; n = 8). Hemodynamic parameters(systolic, diastolic, and mean arterial blood pressures) and the levels of serum bradykinin(BK) and angiotensin Ⅱ(ANGII) were measured at baseline(0 min), and 10 min, 20 min, 30 min, and 45 min after the surgery. Correlation analyses were performed to evaluate the associations between hemodynamic parameters and levels of vasoactive substances.RESULTS:All experimental groups(APH,RVL,and RVHP)showed significant decreases in hemodynamic parameters(systolic,diastolic,and mean arterial blood pressures)compared to baseline levels,as well as compared to the SOP controls(P<0.05 for all).In contrast,BK levels were significantly increased compared to baseline in the APH,RVL,and RVHP groups at all time points measured(P<0.05 for all),whereas no change was observed in the SOP controls.There were no significant differences among the experimental groups for any measure at any time point.Further analyses revealed that systolic,diastolic,and mean arterial blood pressures were all negatively correlated with BK levels,and positively correlated with ANGII levels in the APH,RVL,and RVHP groups(P<0.05 for all).CONCLUSION:In the anhepatic phase of orthotopic liver transplantation,renal vein congestion significantly impacts hemodynamic parameters,which correlate with serum BK and ANGII levels.展开更多
Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abno...Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification.Traditional methods for network traffic classification utilize algorithms such as Naive Bayes,Decision Tree and XGBoost.However,network traffic classification,which is required for network behavior identification,generally suffers from the problem of low accuracy even with the recently proposed deep learning models.To improve network traffic classification accuracy thus improving network intrusion detection rate,this paper proposes a new network traffic classification model,called ArcMargin,which incorporates metric learning into a convolutional neural network(CNN)to make the CNN model more discriminative.ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible.The metric learning regularization feature is called additive angular margin loss,and it is embedded in the object function of traditional CNN models.The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms.According to a set of classification indicators,the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks.Moreover,in open-set tasks,the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.展开更多
In a fractured porous hydrocarbon reservoir,wave velocities and refections depend on frequency and incident angle.A proper description of the frequency dependence of amplitude variations with ofset(AVO)signatures shou...In a fractured porous hydrocarbon reservoir,wave velocities and refections depend on frequency and incident angle.A proper description of the frequency dependence of amplitude variations with ofset(AVO)signatures should allow efects of fracture inflls and attenuation and dispersion of fractured media.The novelty of this study lies in the introduction of an improved approach for the investigation of incident-angle and frequency variations-associated refection responses.The improved AVO modeling method,using a frequency-domain propagator matrix method,is feasible to accurately consider velocity dispersion predicted from frequency-dependent elasticities from a rock physics modeling.And hence,the method is suitable for use in the case of an anisotropic medium with aligned fractures.Additionally,the proposed modeling approach allows the combined contributions of layer thickness,interbedded structure,impedance contrast and interferences to frequency-dependent refection coefcients and,hence,yielding seismograms of a layered model with a dispersive and attenuative reservoir.Our numerical results show bulk modulus of fracture fuid signifcantly afects anisotropic attenuation,hence causing frequencydependent refection abnormalities.These implications indicate the study of amplitude versus angle and frequency(AVAF)variations provides insights for better interpretation of refection anomalies and hydrocarbon identifcation in a layered reservoir with vertical transverse isotropy(VTI)dispersive media.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
Background:The essential roles of platelets in thrombosis have been well recognized.Unexpectedly,thrombosis is prevalent during thrombocytopenia induced by cytotoxicity of biological,physical and chemical origins,whic...Background:The essential roles of platelets in thrombosis have been well recognized.Unexpectedly,thrombosis is prevalent during thrombocytopenia induced by cytotoxicity of biological,physical and chemical origins,which could be suffered by military personnel and civilians during chemical,biological,radioactive,and nuclear events.Especially,thrombosis is considered a major cause of mortality from radiation injury-induced thrombocytopenia,while the underlying pathogenic mechanism remains elusive.Methods:A mouse model of radiation injury-induced thrombocytopenia was built by exposing mice to a sublethal dose of ionizing radiation(IR).The phenotypic and functional changes of platelets and megakaryocytes(MKs)were determined by a comprehensive set of in vitro and in vivo assays,including flow cytometry,flow chamber,histopathology,Western blotting,and chromatin immunoprecipitation,in combination with transcriptomic analysis.The molecular mechanism was investigated both in vitro and in vivo,and was consolidated using MK-specific knockout mice.The translational potential was evaluated using a human MK cell line and several pharmacological inhibitors.Results:In contrast to primitive MKs,mature MKs(mMKs)are intrinsically programmed to be apoptosis-resistant through reprogramming the Bcl-xL-BAX/BAK axis.Interestingly,mMKs undergo minority mitochondrial outer membrane permeabilization(MOMP)post IR,resulting in the activation of the cyclic GMP-AMP synthase-stimulator of IFN genes(cGAS-STING)pathway via the release of mitochondrial DNA.The subsequent interferon-β(IFN-β)response in mMKs upregulates a GTPase guanylate-binding protein 2(GBP2)to produce large and hyperreactive platelets that favor thrombosis.Further,we unmask that autophagy restrains minority MOMP in mMKs post IR.Conclusions:Our study identifies that megakaryocytic mitochondria-cGAS/STING-IFN-β-GBP2 axis serves as a fundamental checkpoint that instructs the size and function of platelets upon radiation injury and can be harnessed to treat platelet pathologies.展开更多
文摘Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12375250,11875121,51977057,11805013)the Natural Science Foundation of Hebei Province,China(Grant Nos.A2020201025 and A2022201036)+3 种基金the Hebei Province Optoelectronic Information Materials Laboratory Performance Subsidy Fund Project(Grant No.22567634H)the Funds for Distinguished Young Scientists of Hebei Province,China(Grant No.A2012201045)the Natural Science Interdisciplinary Research Program of Hebei University(Grant Nos.DXK201908 and DXK202011)the Post-graduate’s Innovation Fund Project of Hebei University(Grant No.HBU2022bs004)。
文摘As a popular approach to producing atmospheric pressure non-thermal plasma,dielectric barrier discharge(DBD)has been extensively used in various application fields.In this paper,DBD with wavy dielectric layers is numerically simulated in atmospheric pressure helium mixed with trace nitrogen based on a fluid model.With varying relative position(phase difference(Δφ))of the wavy surfaces,there is a positive discharge and a negative discharge per voltage cycle,each of which consists of a pulse stage and a hump stage.For the pulse stage,maximal current increases with increasingΔφ.Results show that DBD with the wavy surfaces appears as discrete micro-discharges(MDs),which are self-organized to different patterns with varyingΔφ.The MDs are vertical and uniformly-spaced withΔφ=0,which are self-organized in pairs withΔφ=π/4.These MD pairs are merged into some bright wide MDs withΔφ=π/2.In addition,narrow MDs appear between tilted wide MDs withΔφ=3π/4.WithΔφ=π,the pattern is composed of wide and narrow MDs,which are vertical and appear alternately.To elucidate the formation mechanism of the patterns with differentΔφ,temporal evolutions of electron density and electric field are investigated for the positive discharge.Moreover,surface charge on the wavy dielectric layers has also been compared with differentΔφ.
基金supported by Shanghai Sailing Program(22YF1438700)National Key Research and Development Program of China(2021YFA1201303)+5 种基金National Natural Science Foundation of China(82172511,81972121,81972129,82072521,82011530023,and 82111530200)Sanming Project of Medicine in Shenzhen(SZSM201612078)the Introduction Project of Clinical Medicine Expert Team for Suzhou(SZYJTD201714)Shanghai Talent Development Funding Scheme 2020080Shanghai Sailing Program(21YF1404100 and 22YF1405200)Research Project of Shanghai Science and Technology Commission(22DZ2204900)。
文摘Skeletal muscle has a robust regeneration ability that is impaired by severe injury,disease,and aging.resulting in a decline in skeletal muscle function.Therefore,improving skeletal muscle regeneration is a key challenge in treating skeletal muscle-related disorders.Owing to their significant role in tissue regeneration,implantation of M2 macrophages(M2MФ)has great potential for improving skeletal muscle regeneration.Here,we present a short-wave infrared(SWIR)fluorescence imaging technique to obtain more in vivo information for an in-depth evaluation of the skeletal muscle regeneration effect after M2MФtransplantation.SWIR fluorescence imaging was employed to track implanted M2MФin the injured skeletal muscle of mouse models.It is found that the implanted M2MФaccumulated at the injury site for two weeks.Then,SWIR fluorescence imaging of blood vessels showed that M2MФimplantation could improve the relative perfusion ratio on day 5(1.09±0.09 vs 0.85±0.05;p=0.01)and day 9(1.38±0.16 vs 0.95±0.03;p=0.01)post-injury,as well as augment the degree of skeletal muscle regencration on day 13 post-injury.Finally,multiple linear regression analyses determined that post-injury time and relative perfusion ratio could be used as predictive indicators to evaluate skeletal muscle regeneration.These results provide more in vivo details about M2MФin skeletal muscle regeneration and confirm that M2MФcould promote angiogenesis and improve the degree of skeletal muscle repair,which will guide the research and development of M2MФimplantation to improve skeletal muscle regeneration.
基金supported by the grants from the Ministry of Science and Technology (2016YFA0501000)the National Science Foundation of China to YQZ (31490592) and AY (31271121)
文摘Mutations in long-chain acyl-CoA synthetase 4 (ACSL4) are associated with non-syndromic X-linked intellectual disability (ID). However, the neural functions of ACSL4 and how loss of ACSL4 leads to ID remain largely unexplored. We report here that mutations in Acsl, the Drosophila ortholog of human ACSL3 and ACSL4, result in developmental defects of the mushroom body (MB), the center of olfactory learning and memory. Specifically, Acsl mutants show fewer MB neuroblasts (Nbs) due to reduced proliferation activity and premature differentiation. Consistently, these surviving Nbs show reduced expression of cyclin E, a key regulator of the G1-to S-phase cell cycle transition, and nuclear mislocalization of the transcriptional factor Prospero, which is known to repress self-renewal genes and activate differentiating genes. Furthermore, RNA-seq analysis reveals downregulated Nb-and cell-cyclerelated genes and upregulated neuronal differentiation genes in Acsl mutant Nbs. As Drosophila Acsl and human ACSL4 are functionally conserved, our findings provide novel insights into a critical and previously unappreciated role of Acsl in neurogenesis and the pathogenesis of ACSL4-related ID.
基金Supported by Natural Science Foundation of Gansu Province,China,No.3ZS051-A25-104Clinical Medicine Research Special Funds of Chinese Medical Association,China,No.14040360573
文摘AIM: To explore hemodynamics and vasoactive substance levels during renal vein congestion that occurs in the anhepatic phase of liver transplantation.METHODS: New Zealand rabbits received ligation of the hepatic pedicle, supra-hepatic vena cava and infrahepatic vena cava [anhepatic phase group(APH); n = 8], the renal veins(RVL; n = 8), renal veins and hepatic pedicle [with the inferior vena cava left open)(RVHP; n = 8)], or a sham operation(SOP; n = 8). Hemodynamic parameters(systolic, diastolic, and mean arterial blood pressures) and the levels of serum bradykinin(BK) and angiotensin Ⅱ(ANGII) were measured at baseline(0 min), and 10 min, 20 min, 30 min, and 45 min after the surgery. Correlation analyses were performed to evaluate the associations between hemodynamic parameters and levels of vasoactive substances.RESULTS:All experimental groups(APH,RVL,and RVHP)showed significant decreases in hemodynamic parameters(systolic,diastolic,and mean arterial blood pressures)compared to baseline levels,as well as compared to the SOP controls(P<0.05 for all).In contrast,BK levels were significantly increased compared to baseline in the APH,RVL,and RVHP groups at all time points measured(P<0.05 for all),whereas no change was observed in the SOP controls.There were no significant differences among the experimental groups for any measure at any time point.Further analyses revealed that systolic,diastolic,and mean arterial blood pressures were all negatively correlated with BK levels,and positively correlated with ANGII levels in the APH,RVL,and RVHP groups(P<0.05 for all).CONCLUSION:In the anhepatic phase of orthotopic liver transplantation,renal vein congestion significantly impacts hemodynamic parameters,which correlate with serum BK and ANGII levels.
基金This work was supported by the National Natural Science Foundation of China(61871046).
文摘Attacks on websites and network servers are among the most critical threats in network security.Network behavior identification is one of the most effective ways to identify malicious network intrusions.Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification.Traditional methods for network traffic classification utilize algorithms such as Naive Bayes,Decision Tree and XGBoost.However,network traffic classification,which is required for network behavior identification,generally suffers from the problem of low accuracy even with the recently proposed deep learning models.To improve network traffic classification accuracy thus improving network intrusion detection rate,this paper proposes a new network traffic classification model,called ArcMargin,which incorporates metric learning into a convolutional neural network(CNN)to make the CNN model more discriminative.ArcMargin maps network traffic samples from the same category more closely while samples from different categories are mapped as far apart as possible.The metric learning regularization feature is called additive angular margin loss,and it is embedded in the object function of traditional CNN models.The proposed ArcMargin model is validated with three datasets and is compared with several other related algorithms.According to a set of classification indicators,the ArcMargin model is proofed to have better performances in both network traffic classification tasks and open-set tasks.Moreover,in open-set tasks,the ArcMargin model can cluster unknown data classes that do not exist in the previous training dataset.
基金This work was financially supported by the Science Foundation of China University of Petroleum(Beijing)(2462020YXZZ008)the National Natural Science Foundation of China(41804104,41930425,U19B6003-04-03,41774143)+2 种基金the National Key R&D Program of China(2018YFA0702504)the PetroChina Innovation Foundation(2018D-5007-0303)the Science Foundation of SINOPEC Key Laboratory of Geophysics(33550006-20-ZC0699-0001).
文摘In a fractured porous hydrocarbon reservoir,wave velocities and refections depend on frequency and incident angle.A proper description of the frequency dependence of amplitude variations with ofset(AVO)signatures should allow efects of fracture inflls and attenuation and dispersion of fractured media.The novelty of this study lies in the introduction of an improved approach for the investigation of incident-angle and frequency variations-associated refection responses.The improved AVO modeling method,using a frequency-domain propagator matrix method,is feasible to accurately consider velocity dispersion predicted from frequency-dependent elasticities from a rock physics modeling.And hence,the method is suitable for use in the case of an anisotropic medium with aligned fractures.Additionally,the proposed modeling approach allows the combined contributions of layer thickness,interbedded structure,impedance contrast and interferences to frequency-dependent refection coefcients and,hence,yielding seismograms of a layered model with a dispersive and attenuative reservoir.Our numerical results show bulk modulus of fracture fuid signifcantly afects anisotropic attenuation,hence causing frequencydependent refection abnormalities.These implications indicate the study of amplitude versus angle and frequency(AVAF)variations provides insights for better interpretation of refection anomalies and hydrocarbon identifcation in a layered reservoir with vertical transverse isotropy(VTI)dispersive media.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
基金supported by the Key Program of the National Natural Science Foundation of China(81930090)the National Science Foundation for Distinguished Young Scholars of China(81725019)+2 种基金the National Natural Science Foundation of China(32171104,82273571,and 81874256)the Chongqing Natural Science Foundation(2023NSCQ-MSX0687,CSTB2023NSCQ-JQX0015,and cstc2015jcyjys10001)the Chongqing Talent Program(CQYC20220511002).
文摘Background:The essential roles of platelets in thrombosis have been well recognized.Unexpectedly,thrombosis is prevalent during thrombocytopenia induced by cytotoxicity of biological,physical and chemical origins,which could be suffered by military personnel and civilians during chemical,biological,radioactive,and nuclear events.Especially,thrombosis is considered a major cause of mortality from radiation injury-induced thrombocytopenia,while the underlying pathogenic mechanism remains elusive.Methods:A mouse model of radiation injury-induced thrombocytopenia was built by exposing mice to a sublethal dose of ionizing radiation(IR).The phenotypic and functional changes of platelets and megakaryocytes(MKs)were determined by a comprehensive set of in vitro and in vivo assays,including flow cytometry,flow chamber,histopathology,Western blotting,and chromatin immunoprecipitation,in combination with transcriptomic analysis.The molecular mechanism was investigated both in vitro and in vivo,and was consolidated using MK-specific knockout mice.The translational potential was evaluated using a human MK cell line and several pharmacological inhibitors.Results:In contrast to primitive MKs,mature MKs(mMKs)are intrinsically programmed to be apoptosis-resistant through reprogramming the Bcl-xL-BAX/BAK axis.Interestingly,mMKs undergo minority mitochondrial outer membrane permeabilization(MOMP)post IR,resulting in the activation of the cyclic GMP-AMP synthase-stimulator of IFN genes(cGAS-STING)pathway via the release of mitochondrial DNA.The subsequent interferon-β(IFN-β)response in mMKs upregulates a GTPase guanylate-binding protein 2(GBP2)to produce large and hyperreactive platelets that favor thrombosis.Further,we unmask that autophagy restrains minority MOMP in mMKs post IR.Conclusions:Our study identifies that megakaryocytic mitochondria-cGAS/STING-IFN-β-GBP2 axis serves as a fundamental checkpoint that instructs the size and function of platelets upon radiation injury and can be harnessed to treat platelet pathologies.
