Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world ne...Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.展开更多
The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniqu...The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.展开更多
Using the deep eutectic solvent formed of oxalic acid and choline chloride, a new charge-assisted hydrogen-bonded host framework [Co（en）312[Zr2（C2O4）7]·2H2O （1） has been obtained. The title complex crystall...Using the deep eutectic solvent formed of oxalic acid and choline chloride, a new charge-assisted hydrogen-bonded host framework [Co（en）312[Zr2（C2O4）7]·2H2O （1） has been obtained. The title complex crystallizes in the monoclinic, space group P21/n （No. 14） with a = 7.7448（10）, b = 14.5683（19）, c = 19.375（3） A, fl = 92.124（2）°, V= 2184.5（5） A3, Z = 4, Dc = 1.996 gcm-3, F（000） = 1332, μ = 1.328 mm＂1, R = 0.0353 and wR = 0.0718 （1 〉 2α（I））. Single-crystal structure analysis reveals that the title complex possesses a 3D network assembled through a multitude of charge-assisted hydrogen bonds between the in situ generated anionic coordination complexes [Zr2（C204）7]6- and metal complexes Co（en）33＋.展开更多
Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a sum...Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.展开更多
基金This research was partially supported by the National Natural Science Foundation of China(Grants Nos.U1605251 and 61727809).
文摘Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.
文摘The exponential growth of Internet of Things(IoT)and 5G networks has resulted in maximum users,and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario,cognitive radio techniques such as spectrum sensing,spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index,frequency bands,coding rate etc.,to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency,high power consumption and more chip area utilisation.To overcome the problems mentioned above,we present DEEP HOLE Radio sys-tems,the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency(RF)environment.DEEP HOLE Radio provides(i)an opti-mized deep learning framework with a good trade-off between latency,power and utilization.(ii)Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni direc-tional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models,which run parallel on Zynq-SoC 7000,consuming less area,power,latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn,Long Term Short Memory(LSTM),Convolutional Neural Networks(CNN)and Deep Neural Networks(DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area,power and time.More-over,the experimental results show that the proposed framework decreases the delay,power and area by 15%,20%25%concerning the existing RFlearn and other hardware constraint frameworks.
基金supported by the National Natural Science Foundation of China(Nos.21301024,21103017)the Fundamental Research Funds for the Central Universities(N120305003)
文摘Using the deep eutectic solvent formed of oxalic acid and choline chloride, a new charge-assisted hydrogen-bonded host framework [Co（en）312[Zr2（C2O4）7]·2H2O （1） has been obtained. The title complex crystallizes in the monoclinic, space group P21/n （No. 14） with a = 7.7448（10）, b = 14.5683（19）, c = 19.375（3） A, fl = 92.124（2）°, V= 2184.5（5） A3, Z = 4, Dc = 1.996 gcm-3, F（000） = 1332, μ = 1.328 mm＂1, R = 0.0353 and wR = 0.0718 （1 〉 2α（I））. Single-crystal structure analysis reveals that the title complex possesses a 3D network assembled through a multitude of charge-assisted hydrogen bonds between the in situ generated anionic coordination complexes [Zr2（C204）7]6- and metal complexes Co（en）33＋.
文摘Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.
