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STCF conceptual design report (Volume 1): Physics & detector

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Frontiers of physics 2024年第1期19卷 1-154页

作者： M.Achasov X.C.Ai L.P.An R.Aliberti Q.An X.Z.Bai y.Bai O.Bakina A.Barnyakov V.Blinov V.Bobrovnikov D.Bodrov A.Bogomyagkov A.Bondar I.Boyko Z.H.Bu F.M.Cai H.Cai J.J.Cao Q.H.Cao X.Cao Z.Cao Q.Chang K.T.Chao D.y.Chen H.Chen H.X.Chen J.F.Chen K.Chen L.L.Chen P.Chen S.L.Chen S.M.Chen S.Chen S.P.Chen W.Chen X.Chen X.F.Chen X.R.Chen y.Chen y.Q.Chen H.y.Cheng J.Cheng S.Cheng T.G.Cheng J.P.Dai L.y.Dai X.C.Dai D.Dedovich A.Denig I.Denisenko J.M.Dias D.Z.Ding L.y.Dong W.H.Dong V.Druzhinin D.S.Du y.J.Du Z.G.Du L.M.Duan D.Epifanov y.L.Fan S.S.Fang Z.J.Fang G.Fedotovich C.Q.Feng X.Feng y.T.Feng J.L.Fu J.Gao y.N.Gao P.S.Ge C.Q.Geng L.S.Geng A.Gilman L.Gong T.Gong B.Gou W.Gradl J.L.Gu A.Guevara L.C.Gui A.Q.Guo F.K.Guo J.C.Guo J.Guo y.P.Guo Z.H.Guo A.Guskov K.L.Han L.Han M.Han X.Q.Hao J.B.He S.Q.He X.G.He y.L.He Z.B.He Z.X.Heng B.L.Hou T.J.Hou y.R.Hou C.y.Hu H.M.Hu K.Hu R.J.Hu W.H.Hu X.H.Hu y.C.Hu J.Hua G.S.Huang J.S.Huang M.Huang Q.y.Huang W.Q.Huang X.T.Huang X.J.Huang y.B.Huang y.S.Huang N.Hüsken V.Ivanov Q.P.Ji J.J.Jia S.Jia Z.K.Jia H.B.Jiang J.Jiang S.Z.Jiang J.B.Jiao Z.Jiao H.J.Jing X.L.Kang X.S.Kang B.C.Ke M.Kenzie A.Khoukaz I.Koop E.Kravchenko A.Kuzmin y.lei E.Levichev C.H.Li C.Li D.y.Li F.Li G.Li G.Li H.B.Li H.Li H.N.Li H.J.Li H.L.Li J.M.Li J.Li L.Li L.Li L.y.Li N.Li P.R.Li R.H.Li S.Li T.Li W.J.Li X.Li X.H.Li X.Q.Li X.H.Li y.Li y.y.Li Z.J.Li H.Liang J.H.Liang y.T.Liang G.R.Liao L.Z.Liao y.Liao C.X.Lin D.X.Lin X.S.Lin B.J.Liu C.W.Liu D.Liu F.Liu G.M.Liu H.B.Liu J.Liu J.J.Liu J.B.Liu K.Liu K.y.Liu K.Liu L.Liu Q.Liu S.B.Liu T.Liu X.Liu y.W.Liu y.Liu y.L.Liu Z.Q.Liu Z.y.Liu Z.W.Liu I.Logashenko y.Long C.G.Lu J.X.Lu N.Lu Q.F.Lü y.Lu y.Lu Z.Lu P.Lukin F.J.Luo T.Luo X.F.Luo y.H.Luo H.J.Lyu X.R.Lyu J.P.Ma P.Ma y.Ma y.M.Ma F.Maas S.Malde D.Matvienko Z.X.Meng R.Mitchell A.Nefediev y.Nefedov S.L.Olsen Q.Ouyang P.Pakhlov G.Pakhlova X.Pan y.Pan E.Passemar y.P.Pei H.P.Peng L.Peng X.y.Peng X.J.Peng K.Peters S.Pivovarov E.Pyata B.B.Qi y.Q.Qi W.B.Qian y.Qian C.F.Qiao J.J.Qin J.J.Qin L.Q.Qin X.S.Qin T.L.Qiu J.Rademacker C.F.Redmer H.y.Sang Anhui University Hefei 230039China Beihang University Beijing 100191China Budker Institute of Nuclear Physics Novosibirsk 630090Russia CAS Key Laboratory of Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China Cavendish Laboratory University of CambridgeJJ Thomson AveCambridge CB30HEUnited Kingdom Central China Normal University Wuhan 430079China Central South University Changsha 410083China China University of Geosciences Wuhan 430074China China University of Mining and Technology Xuzhou 221116China École Polytechnique Fédérale de Lausanne(EPFL) LausanneSwitzerland Fudan University Shanghai 200433China Goethe University Frankfurt D-60325 Frankfurt am MainGermany Guangxi Normal University Guilin 541004China Guangxi Uninversity Nanning 530004China Hangzhou Institute for Advanced Study UCASHangzhou 310024China Hebei Normal University Shijiazhuang 050024China Hebei University Baoding 071002China Hefei University of Technology Hefei 230601China Helmholtz Institute Mainz Staudinger Weg 18D-55099 MainzGermany Henan Normal University Xinxiang 453007China Henan University Kaifeng 475004China High Energy Physics Center Chung-Ang UniversitySeoul 06974Korea Higher School of Economy 11 Pokrovsky Bulvar Moscow 109028Russia Huangshan University Huangshan 245000China Hubei University of Automotive Technology Shiyan 442002China Hunan Normal University Changsha 410081China Hunan University of Science and Technology Xiangtan 411201China Hunan University Changsha 410082China Indiana University BloomingtonIndiana 47405USA Inner Mongolia University Hohhot 010021China Institute of Advanced Science Facilities Shenzhen 518107China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China Institute of Physics Academia SinicaTaipeiTaiwan 11529China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China Jilin University Changch

The superτ-charm facility(STCF)is an electron–positron collider proposed by the Chinese particle physics community.It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5×10^(35) cm^(–2)·s^(–1) or higher.The STCF will produce a data sample about a factor of 100 larger than that of the presentτ-charm factory—the BEPCII,providing a unique platform for exploring the asymmetry of matter-antimatter(charge-parity violation),in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions,as well as searching for exotic hadrons and physics beyond the Standard Model.The STCF project in China is under development with an extensive R&D program.This document presents the physics opportunities at the STCF,describes conceptual designs of the STCF detector system,and discusses future plans for detector R&D and physics case studies.

关键词： electron–positron collider tau-charm region high luminosity STCF detector conceptual design

Recent advances in cancer-associated fibroblast:Biomarkers,signaling pathways,and therapeutic opportunities

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Chinese Medical Journal 2024年第6期137卷 638-650页

作者： Donger Zhou lei Zheng Department of Hepatobiliary-Pancreatic Surgery The Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang 310009China Sidney Kimmel Comprehensive Cancer Center The Skip Viragh Pancreatic Cancer Center for Clinical Research and Careand The Bloomberg-Kimmel Institute for Immunotherapy at Johns Hopkins University School of MedicineBaltimoreMD 21287USA

Anti-cancer therapies usually focus on tumor cells,but non-tumor stromal components in the tumor microenvironment also play vital roles in tumor initiation and progression,which may be the prognostic factors and potential therapeutic targets.Cancer-associated fibroblasts(CAFs)are the essential component in the tumor environment,exhibiting high heterogeneity in their cell origin and phenotype with diverse functions that influence tumor angiogenesis,immune systems,and metabolism.Single-cell RNA sequencing and genetically engineered mouse models have increased our understanding of CAF diversity,and many subtypes have been defined.However,the precise functions of these subtypes need to be studied and validated.Studies of signaling pathways and epigenetic changes in CAFs facilitate understanding of the phenotypes of CAFs and the crosstalk between tumor cells and CAFs to provide potential therapeutic targets.Some clinical trials,including phase III trials targeting CAFs,have been performed recently.However,few of these trials have generated promising results,which indicates that the complexity of CAFs in the tumor microenvironment remains largely unknown,and in-depth investigations of CAFs should be performed.This review summarizes the research on CAFs,focusing on the heterogeneity of their phenotypes and functions,specific signaling pathways,and the therapeutic strategies involving CAFs.Additionally,we briefly discuss the current technologies commonly used in CAF studies and describe the challenges and future perspectives of CAF research.

关键词： Cancer-associated fibroblasts Tumor microenvironment Anti-cancer therapy Targeted therapy

Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

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Tsinghua Science and Technology 2024年第2期29卷 553-575页

作者： lei Chen yuan Li yong lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning models.However,any metapaths consisting of multiple,simple metarelations must be driven by domain experts.These sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this problem.Specifically,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given metapaths.Thereafter,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node type.Next,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link type.Finally,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the models.Extensive experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

Structural energy storage composites based on modified carbon fiber electrode with metal-organic frame enhancing layered double hydroxide

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Nano Research 2024年第3期17卷 1552-1563页

作者： Jinrui ye Zhongbao Wang Qin lei lei Sun Jinfeng Gu School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Tangshan Research Institute Beijing Institute of TechnologyTangshan 063000China School of Materials Science and Engineering Shenyang Jianzhu UniversityShenyang 110168China Department of Stomatology PLA Strategic Support Force Characteristic Medical CenterBeijing 100101China Beijing Electromechanical Engineering Research Institute Beijing 100074China

Structural energy storage composites present advantages in simultaneously achieving structural strength and electrochemical properties.Adoption of carbon fiber electrodes and resin structural electrolytes in energy storage composite poses challenges in maintaining good mechanical and electrochemical properties at reasonable cost and effort.Here,we report a simple method to fabricate structural supercapacitor using carbon fiber electrodes(modified by Ni-layered double hydroxide(Ni-LDH)and in-situ growth of Co-metal-organic framework(Co-MOF)in a two-step process denoted as Co-MOF/Ni-LDH@CF)and bicontinuous-phase epoxy resin-based structural electrolyte.Co-MOF/Ni-LDH@CF as electrode material exhibits improved specific capacity(42.45 F·g^(-1))and cycle performance(93.3%capacity retention after 1000 cycles)in a three-electrode system.The bicontinuous-phase epoxy resin-based structural electrolyte exhibits an ionic conductivity of 3.27×10^(-4) S·cm^(-1).The fabricated Co-MOF/Ni-LDH@CF/SPE-50 structural supercapacitor has an energy density of 3.21 Wh·kg^(-1) at a power density of 42.25 W·kg^(-1),whilst maintaining tensile strength and modulus of 334.6 MPa and 25.2 GPa.These results show practical potential of employing modified commercial carbon fiber electrodes and epoxy resin-based structural electrolytes in structural energy storage applications.

关键词： carbon fiber electrode structural electrolyte structural energy storage composites supercapacitor

DCVAE-adv:A Universal Adversarial Example Generation Method for White and Black Box Attacks

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Tsinghua Science and Technology 2024年第2期29卷 430-446页

作者： lei Xu Junhai Zhai College of Mathematics and Information Science Hebei UniversityBaoding 071002China

Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original images.In this paper,we propose a novel adversarial example generation method,called DCVAE-adv.Different from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images.Furthermore,the proposed method can be applied to both white box and black box attacks.In addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory overhead.We compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack.Our code is available at https://***/xzforeverlove/DCVAE-adv.

关键词： deep neural network adversarial examples white box attack black box attack robustness

Pharmacological Effects of Botanical Drugs on Myocardial Metabolism in Chronic Heart Failure

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Chinese Journal of Integrative Medicine 2024年第5期30卷 458-467页

作者： HU yu-xuan QIU Sheng-lei SHANG Ju-ju WANG Zi LAI Xiao-lei Beijing Hospital of Traditional Chinese Medicine Capital Medical UniversityBeijing 100010China

Although there have been significant advances in the treatment of heart failure in recent years,chronic heart failure remains a leading cause of cardiovascular disease-related death.Many studies have found that targeted cardiac metabolic remodeling has good potential for the treatment of heart failure.However,most of the drugs that increase cardiac energy are still in the theoretical or testing stage.Some research has found that botanical drugs not only increase myocardial energy metabolism through multiple targets but also have the potential to restore the balance of myocardial substrate metabolism.In this review,we summarized the mechanisms by which botanical drugs(the active ingredients/formulas/Chinese patent medicines)improve substrate utilization and promote myocardial energy metabolism by activating AMP-activated protein kinase(AMPK),peroxisome proliferator-activated receptors(PPARs)and other related targets.At the same time,some potential protective effects of botanical drugs on myocardium,such as alleviating oxidative stress and dysbiosis signaling,caused by metabolic disorders,were briefly discussed.

关键词： botanical drugs chronic heart failure myocardial metabolism pharmacological effects review

Dynamic multifunctional metasurfaces:an inverse design deep learning approach

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Photonics Research 2024年第1期12卷 123-133页

作者： ZHI-DAN lei yI-DUO XU CHENG lei yAN ZHAO DU WANG The Institute of Technological Sciences Wuhan UniversityWuhan 430072China College of Materials Science and Engineering Sichuan UniversityChengdu 610065China

Optical metasurfaces(OMs)offer unprecedented control over electromagnetic waves,enabling advanced optical multiplexing.The emergence of deep learning has opened new avenues for designing OMs.However,existing deep learning methods for OMs primarily focus on forward design,which limits their design capabilities,lacks global optimization,and relies on prior knowledge.Additionally,most OMs are static,with fixed functionalities once processed.To overcome these limitations,we propose an inverse design deep learning method for dynamic OMs.Our approach comprises a forward prediction network and an inverse retrieval network.The forward prediction network establishes a mapping between meta-unit structure parameters and reflectance spectra.The inverse retrieval network generates a library of meta-unit structure parameters based on target requirements,enabling end-to-end design of OMs.By incorporating the dynamic tunability of the phase change material Sb2Te3with inverse design deep learning,we achieve the design and verification of dynamic multifunctional OMs.Our results demonstrate OMs with multiple information channels and encryption capabilities that can realize multiple physical field optical modulation functions.When Sb2Te3is in the amorphous state,near-field nano-printing based on meta-unit amplitude modulation is achieved for X-polarized incident light,while holographic imaging based on meta-unit phase modulation is realized for circularly polarized light.In the crystalline state,the encrypted information remains secure even with the correct polarization input,achieving double encryption.This research points towards ultra-compact,high-capacity,and highly secure information storage approaches.

关键词： polarization inverse approach

Two-Stage Online Debiased Lasso Estimation and Inference for High-Dimensional Quantile Regression with Streaming Data

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Journal of Systems Science & Complexity 2024年第3期37卷 1251-1270页

作者： PENG yanjin WANG lei School of Statistics and Data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

In this paper,the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression(QR)models in the presence of streaming data.In the first stage,the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms.The estimation process only involves the current data batch and specific historical summary statistics,which perfectly accommodates to the special structure of streaming data.In the second stage,an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established.The authors conduct extensive numerical experiments to evaluate the performance of the proposed method.These experiments demonstrate the effectiveness of the proposed method and support the theoretical results.An application to the Beijing PM2.5 Dataset is also presented.

关键词： Adaptive tuning asymptotic normality debiased lasso online updating quantile regres-sion

Multi-Scale-Matching neural networks for thin plate bending problem

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Theoretical & Applied Mechanics Letters 2024年第1期14卷 11-15页

作者： lei Zhang Guowei He The State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Engineering Science University of Chinese Academy of SciencesBeijing 100049China

Physics-informed neural networks are a useful machine learning method for solving differential equations,but encounter challenges in effectively learning thin boundary layers within singular perturbation problems.To resolve this issue,multi-scale-matching neural networks are proposed to solve the singular perturbation problems.Inspired by matched asymptotic expansions,the solution is decomposed into inner solutions for small scales and outer solutions for large scales,corresponding to boundary layers and outer regions,respectively.Moreover,to conform neural networks,we introduce exponential stretched variables in the boundary layers to avoid semiinfinite region problems.Numerical results for the thin plate problem validate the proposed method.

关键词： Singular perturbation Physics-informed neural networks Boundary layer Machine learning