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University",[114,135],"{\"ror\":\"02hpadn98\",\"openalex\":\"I20121455\"}","2026-05-14T00:59:39.30202501","2026-06-01T05:12:38.352653132","companyType","UNIVERSITY","foundedDate","headquarters","Bielefeld, Germany","country","DE","employeeCount","fundingStage","totalFunding","valuation","website","https://www.uni-bielefeld.de","stockSymbol","uscc","crunchbaseUrl","Universität Bielefeld","company_I205783295","Cornell University",[137,143],"{\"ror\":\"05bnh6r87\",\"openalex\":\"I205783295\"}","2026-05-14T00:28:38.363586078","2026-06-01T04:12:24.892295408","US","Universidad Cornell","company_I99065089","Tsinghua University",[145,151],"{\"ror\":\"03cve4549\",\"openalex\":\"I99065089\"}","2026-05-14T00:28:38.40459851","2026-06-01T04:14:47.734032813","CN","清华大学","company_I27357992","Dalian University of Technology","大连理工大学",[153,161,154],"{\"ror\":\"023hj5876\",\"openalex\":\"I27357992\",\"wikidata\":\"https://www.wikidata.org/wiki/Q1477368\"}","2026-05-14T00:38:50.232934058","2026-06-01T04:36:39.30086726","Dalian, China","https://www.dlut.edu.cn","Dàlián Lǐgōng Dàxué","company_I1174212","University of Southern California",[170,163,171],"{\"ror\":\"03taz7m60\",\"openalex\":\"I1174212\",\"wikidata\":\"Q4614\"}","2026-05-14T00:32:31.235823591","2026-06-01T04:20:45.745224088","Los Angeles, United States","https://usc.edu","Universidad del Sur de California","Université de Californie du Sud","company_I4210116219","Engineering (Italy)",[173],"{\"ror\":\"045s9b323\",\"openalex\":\"I4210127672\"}","2026-05-14T02:31:23.240051679","2026-06-01T08:35:31.276120109","COMPANY","Rome, Italy","IT","http://www.eng.it/","company_I76130692","Zhejiang University","浙江大学",[191,192,183,184],"{\"ror\":\"00a2xv884\",\"openalex\":\"I76130692\"}","2026-05-14T00:31:58.371782647","2026-06-01T04:15:30.652921181","Hangzhou, China","https://www.zju.edu.cn","Chekiang University","Zheda","company_I126520041","University of Science and Technology of China",[194,199],"{\"ror\":\"04c4dkn09\",\"openalex\":\"I126520041\",\"wikidata\":\"Q1074103\"}","2026-05-14T00:28:02.350094705","2026-06-01T04:25:33.69671061","中国科学技术大学","company_I4210124140","CAS Key Laboratory of Urban Pollutant Conversion","中国科学院城市污染物转化重点实验室",[201,202],"{\"ror\":\"02w5xy446\",\"openalex\":\"I4210124140\"}","2026-05-14T04:03:41.372896709","2026-06-01T08:53:45.122509003","RESEARCH_INSTITUTE","Amoy, China","http://www.iue.ac.cn/jgsz/kyjg/wrzhzd/","company_I4210150858","Beijing Academy of Quantum Information Sciences",[211],"{\"ror\":\"04nqf9k60\",\"openalex\":\"I4210150858\"}","2026-05-14T04:45:20.449939758","2026-06-01T11:30:37.015560776","Beijing, China","https://www.baqis.ac.cn",{"_15":331,"_29":332,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":333,"_39":-5,"_40":41,"_42":334,"_44":335,"_230":336,"_232":-5,"_233":337,"_48":332,"_235":338,"_237":339,"_239":340,"_241":242,"_52":341,"_244":-5,"_245":342,"_247":-5,"_248":86,"_58":332,"_59":-5},{"_15":315,"_29":316,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":317,"_39":-5,"_40":41,"_42":318,"_44":319,"_230":320,"_232":321,"_233":322,"_48":316,"_235":323,"_237":324,"_239":325,"_241":326,"_52":327,"_244":-5,"_245":328,"_247":329,"_248":330,"_58":316,"_59":-5},{"_15":299,"_29":300,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":301,"_39":-5,"_40":41,"_42":302,"_44":303,"_230":304,"_232":-5,"_233":305,"_48":300,"_235":306,"_237":307,"_239":308,"_241":309,"_52":310,"_244":-5,"_245":311,"_247":-5,"_248":86,"_58":300,"_59":-5},{"_15":281,"_29":282,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":283,"_39":-5,"_40":41,"_42":284,"_44":285,"_230":286,"_232":287,"_233":288,"_48":282,"_235":289,"_237":290,"_239":291,"_241":292,"_52":293,"_244":-5,"_245":294,"_247":295,"_248":86,"_58":282,"_59":-5},{"_15":268,"_29":269,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":270,"_39":-5,"_40":41,"_42":271,"_44":272,"_230":273,"_232":-5,"_233":274,"_48":269,"_235":-5,"_237":275,"_239":276,"_241":277,"_52":278,"_244":-5,"_245":279,"_247":-5,"_248":86,"_58":269,"_59":-5},{"_15":252,"_29":253,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":254,"_39":-5,"_40":41,"_42":255,"_44":256,"_230":257,"_232":-5,"_233":258,"_48":253,"_235":259,"_237":260,"_239":261,"_241":-5,"_52":262,"_244":-5,"_245":263,"_247":264,"_248":86,"_58":253,"_59":-5},{"_15":225,"_29":226,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":227,"_39":-5,"_40":41,"_42":228,"_44":229,"_230":231,"_232":-5,"_233":234,"_48":226,"_235":236,"_237":238,"_239":240,"_241":242,"_52":243,"_244":-5,"_245":246,"_247":-5,"_248":86,"_58":226,"_59":-5},"paper_W1990531224","New results in binary multiple descriptions","{\"openalex\":\"W1990531224\",\"doi\":\"10.1109/tit.1987.1057330\"}","2026-05-23T11:04:18.239984315","2026-05-23T11:04:18.239986127","doi","10.1109/tit.1987.1057330","arxivId","openalexId","W1990531224","abstractText","An encoder whose input is a binary equiprobable memoryless source produces one output of rate R_{1} and another of rate R_{2} . Let D_{1}, D_{2}, and D_{0} , respectively, denote the average error frequencies with which the source data can be reproduced on the basis of the encoder output of rate R_{l} only, the encoder output of rate R_{2} only, and both encoder outputs. The two-descriptions problem is to determine the region R of all quintuples (R_{1}, R_{2}, D_{1}, D_{2}, D_{0}) that are achievable in thc usual Shannon sense. Let R(D)=1+D \\log_{2} D+(1-D) \\log_{2}(1-D) denote the error frequency rate-distortion function of the source. The \"no excess rate case\" prevails when R_{1} + R_{2} = R(D_{0}) , and the \"excess rate case\" when R_{1} + R_{2} > R(D_{0}) . Denote the section of R at (R_{1}, R_{2}, D_{0}) by D(R_{1} R_{2}, D_{0}) =\\{(D_{1},D_{2}): (R_{1}, R_{2}, D_{1},D_{2},D_{0}) \\in R} . In the no excess rate case we show that a portion of the boundary of D(R_{1}, R_{2}, D_{0}) coincides with the curve (\\frac{1}{2} + D_{1}-2D_{0})(\\frac_{1}_{2} + D_{2}-2D_{0})= \\frac{1}{2}(1-2D_{0})^{2} . This curve is an extension of Witsenhausen's hyperbola bound to the case D_{0} > 0 . It follows that the projection of R onto the (D_{1}, D_{2}) -plane at fixed D_{0} consists of all D_{1} \\geq D_{0} and D_{2} \\geq D_{0} that lie on or above this hyperbola. In the excess rate case we show by counterexample that the achievable region of El Gamal and Cover is not tight.","publishedDate","1987-07-01","publishedYear",1987,"venue","IEEE Transactions on Information Theory",216,"authors","keywords",[249,250,251],"pdfUrl","topVenue","Wireless Communication Security Techniques","Algorithms and Data Compression","Distributed Sensor Networks and Detection Algorithms","paper_W2808466528","ANRL: Attributed Network Representation Learning via Deep Neural Networks","{\"openalex\":\"W2808466528\",\"doi\":\"10.24963/ijcai.2018/438\"}","2026-05-22T21:23:49.189505685","2026-05-22T21:23:49.189507522","10.24963/ijcai.2018/438","W2808466528","Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network. Though network RL has been intensively studied, most existing works focus on either network structure or node attribute information. In this paper, we propose a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way. Specifically, we propose a neighbor enhancement autoencoder to model the node attribute information, which reconstructs its target neighbors instead of itself. To capture the network structure, attribute-aware skip-gram model is designed based on the attribute encoder to formulate the correlations between each node and its direct or indirect neighbors. We conduct extensive experiments on six real-world networks, including two social networks, two citation networks and two user behavior networks. The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.","2018-07-01",2018,235,[265,266,267],"https://www.ijcai.org/proceedings/2018/0438.pdf","Advanced Graph Neural Networks","Complex Network Analysis Techniques","Opinion Dynamics and Social Influence","paper_W3040745443","Consensus reaching for social network group decision making by considering leadership and bounded confidence","{\"openalex\":\"W3040745443\",\"doi\":\"10.1016/j.knosys.2020.106240\"}","2026-05-22T08:19:12.197512511","2026-05-22T08:19:12.19751457","10.1016/j.knosys.2020.106240","W3040745443","2020-07-09",2020,"Knowledge-Based Systems",255,[267,266,280],"Expert finding and Q&A systems","paper_W1969395616","Measurement-Device-Independent Quantum Key Distribution over 200 km","{\"arxiv\":\"1407.8012\",\"openalex\":\"W1969395616\",\"doi\":\"10.1103/physrevlett.113.190501\"}","2026-05-21T18:33:09.201704055","2026-05-21T18:33:09.208500404","10.1103/physrevlett.113.190501","1407.8012","W1969395616","Measurement-device-independent quantum key distribution (MDIQKD) protocol is immune to all attacks on detection and guarantees the information-theoretical security even with imperfect single-photon detectors. Recently, several proof-of-principle demonstrations of MDIQKD have been achieved. Those experiments, although novel, are implemented through limited distance with a key rate less than 0.1 bit/s. Here, by developing a 75 MHz clock rate fully automatic and highly stable system and superconducting nanowire single-photon detectors with detection efficiencies of more than 40%, we extend the secure transmission distance of MDIQKD to 200 km and achieve a secure key rate 3 orders of magnitude higher. These results pave the way towards a quantum network with measurement-device-independent security.","2014-11-06",2014,"Physical Review Letters",279,[296,297,298],"https://arxiv.org/pdf/1407.8012","Quantum Information and Cryptography","Quantum Mechanics and Applications","Quantum Computing Algorithms and Architecture","paper_W2122051577","A new initialization technique for generalized Lloyd iteration","{\"openalex\":\"W2122051577\",\"doi\":\"10.1109/97.329844\"}","2026-05-20T00:07:58.254238581","2026-05-20T00:07:58.254500256","10.1109/97.329844","W2122051577","The generalized Lloyd algorithm plays an important role in the design of vector quantizers (VQ) and in feature clustering for pattern recognition. In the VQ context, this algorithm provides a procedure to iteratively improve a codebook and results in a local minimum that minimizes the average distortion function. We propose an efficient method to obtain a good initial codebook that can accelerate the convergence of the generalized Lloyd algorithm and achieve a better local minimum as well.< >","1994-10-01",1994,"IEEE Signal Processing Letters",369,[312,313,314],"Advanced Data Compression Techniques","Image and Signal Denoising Methods","Advanced Adaptive Filtering Techniques","paper_W2247946197","Quantum random number generation","{\"arxiv\":\"1510.08957\",\"openalex\":\"W2247946197\",\"doi\":\"10.1038/npjqi.2016.21\"}","2026-05-19T11:41:54.200414153","2026-05-19T11:41:54.200946291","10.1038/npjqi.2016.21","1510.08957","W2247946197","Abstract Quantum physics can be exploited to generate true random numbers, which have important roles in many applications, especially in cryptography. Genuine randomness from the measurement of a quantum system reveals the inherent nature of quantumness—coherence, an important feature that differentiates quantum mechanics from classical physics. The generation of genuine randomness is generally considered impossible with only classical means. On the basis of the degree of trustworthiness on devices, quantum random number generators (QRNGs) can be grouped into three categories. The first category, practical QRNG, is built on fully trusted and calibrated devices and typically can generate randomness at a high speed by properly modelling the devices. The second category is self-testing QRNG, in which verifiable randomness can be generated without trusting the actual implementation. The third category, semi-self-testing QRNG, is an intermediate category that provides a tradeoff between the trustworthiness on the device and the random number generation speed.","2016-06-27",2016,"npj Quantum Information",416,[297,298,296],"https://arxiv.org/pdf/1510.08957",true,"paper_W2068143054","The CEO problem [multiterminal source coding]","{\"openalex\":\"W2068143054\",\"doi\":\"10.1109/18.490552\"}","2026-05-16T05:48:22.227974553","2026-05-19T02:46:52.196889944","10.1109/18.490552","W2068143054","We consider a new problem in multiterminal source coding motivated by the following decentralized communication/estimation task. A firm's Chief Executive Officer (CEO) is interested in the data sequence {X(t)}/sub t=1//sup /spl infin// which cannot be observed directly, perhaps because it represents tactical decisions by a competing firm. The CEO deploys a team of L agents who observe independently corrupted versions of {X(t)}/sub t=1//sup /spl infin//. Because {X(t)} is only one among many pressing matters to which the CEO must attend, the combined data rate at which the agents may communicate information about their observations to the CEO is limited to, say, R bits per second. If the agents were permitted to confer and pool their data, then in the limit as L/spl rarr//spl infin/ they usually would be able to smooth out their independent observation noises entirely. Then they could use their R bits per second to provide the CEO with a representation of {X(t)} with fidelity D(R), where D(/spl middot/) is the distortion-rate function of {X(t)}. In particular, with such data pooling D can be made arbitrarily small if R exceeds the entropy rate H of {X(t)}. Suppose, however, that the agents are not permitted to convene, Agent i having to send data based solely on his own noisy observations {Y/sub i/(t)}. We show that then there does not exist a finite value of R for which even infinitely many agents can make D arbitrarily small. 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Zhen Zhang 人物

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