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arXiv 2018年

作者： Yang, Jianhua Liu, Kai Kang, Xiangui Wong, Edward K. Shi, Yun-Qing Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Department of Computer Science and Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Department of ECE New Jersey Institute of Technology NewarkNJ07102 United States

With the recent development of deep learning on steganalysis, embedding secret information into digital images faces great challenges. In this paper, a secure steganography algorithm by using adversarial training is proposed. The architecture contain three component modules: a generator, an embedding simulator and a discriminator. A generator based on U-NET to translate a cover image into an embedding change probability is proposed. To fit the optimal embedding simulator and propagate the gradient, a function called Tanh-simulator is proposed. As for the discriminator, the selection-channel awareness (SCA) is incorporated to resist the SCA based steganalytic methods. Experimental results have shown that the proposed framework can increase the security performance dramatically over the recently reported method ASDL-GAN, while the training time is only 30% of that used by ASDL-GAN. Furthermore, it also performs better than the hand-crafted steganographic algorithm S-UNIWARD. Copyright © 2018, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Preventing "bad" content dispersal in Named data networking 17

Preventing "bad" content dispersal in Named Data Networking

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Wang, Yi Qi, Zhuyun Lei, Kai Liu, Bin Tian, Chen Huawei Future Network Theory Lab Hong Kong School of Electronics and Computer Engineering Peking University Shenzhen518055 China Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084 China State Key Laboratory for Novel Software Technology Nanjing University Najing210023 China

ISBN: (纸本)9781450348737

Named data networking (NDN) improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network, NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents, the speed of content verification is too slow to satisfy the high speed requirement. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted", leads to the best performance, but can be bypassed if the clients and the content producer collude. To prevent the aforementioned collusion attack, we improve the user-assisted content verification scheme and propose the second content verification scheme, named "Router-Cooperation", in which the edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm. The simulation results demonstrate that this Router-Cooperation scheme can speed up 145.5× (in hardware) and 18.85× (in software) of the original content verification scheme with merely extra transmission overhead. © 2017 ACM.

关键词： Verification

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The first JWST spectrum of a GRB afterglow: No bright supernova in observations of the brightest GRB of all time, GRB 221009A

arXiv

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arXiv 2023年

作者： Levan, A.J. Lamb, G.P. Schneider, B. Hjorth, J. Zafar, T. Postigo, A. de Ugarte Sargent, B. Mullally, S.E. Izzo, L. D’Avanzo, P. Burns, E. Fernández, J.F. Agüí Barclay, T. Bernardini, M.G. Bhirombhakdi, K. Bremer, M. Brivio, R. Campana, S. Chrimes, A.A. D’Elia, V. Della Valle, M. De Pasquale, M. Ferro, M. Fong, W. Fruchter, A.S. Fynbo, J.P.U. Gaspari, N. Gompertz, B.P. Hartmann, D.H. Hedges, C.L. Heintz, K.E. Hotokezaka, K. Jakobsson, P. Kann, D.A. Kennea, J.A. Laskar, T. Floc’h, E. Le Malesani, D.B. Melandri, A. Metzger, B.D. Oates, S.R. Pian, E. Piranomonte, S. Pugliese, G. Racusin, J.L. Rastinejad, J.C. Ravasio, M.E. Rossi, A. Saccardi, A. Salvaterra, R. Sbarufatti, B. Starling, R.L.C. Tanvir, N.R. Thöne, C.C. van der Horst, A.J. Vergani, S.D. Watson, D. Wiersema, K. Wijers, R.A.M.J. Xu, Dong Department of Astrophysics IMAPP Radboud University Nijmegen6525 AJ Netherlands Department of Physics University of Warwick CoventryCV4 7AL United Kingdom Astrophysics Research Institute Liverpool John Moores University 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen2200 Denmark Australian Astronomical Optics Macquarie University 105 Delhi Road North RydeNSW2113 Australia Artemis Université Côte d’Azur Observatoire de la Côte d’Azur CNRS NiceF-06304 France Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Center for Astrophysical Sciences The William H. Miller III Department of Physics and Astronomy Johns Hopkins University BaltimoreMD21218 United States INAF Osservatorio Astronomico di Brera Via E. Bianchi 46 LC MerateI-23807 Italy Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía s/n Granada18008 Spain NASA Goddard Space Flight Center GreenbeltMD20771 United States University of Maryland Baltimore County BaltimoreMD21250 United States 300 Rue de la Piscine Saint Martin d’HèresF-38406 France Universitá degli Studi dell’Insubria Dipartimento di Scienza e Alta Tecnologia via Valleggio 11 Como22100 Italy ASI Italian Space Agency Space Science Data Centre Via del Politecnico snc Rome00133 Italy Monte Porzio CatoneI-00040 Italy Capodimonte Astronomical Observatory INAF-Napoli Salita Moiariello 16 NapoliI-80131 Italy University of Messina Mathematics Informatics Physics and Earth Science Department Via F.D. D’Alcontres 31 Polo Papardo Messina98166 Italy Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States T

We present JWST and Hubble Space Telescope (HST) observations of the afterglow of GRB 221009A, the brightest gamma-ray burst (GRB) ever observed. This includes the first mid-IR spectra of any GRB, obtained with JWST/NIRSPEC (0.6-5.5 micron) and MIRI (5-12 micron), 12 days after the burst. Assuming that the intrinsic spectral slope is a single power-law, with Fν ∝ ν−β, we obtain β ≈ 0.35, modified by substantial dust extinction with AV = 4.9. This suggests extinction above the notional Galactic value, possibly due to patchy extinction within the Milky Way or dust in the GRB host galaxy. It further implies that the X-ray and optical/IR regimes are not on the same segment of the synchrotron spectrum of the afterglow. If the cooling break lies between the X-ray and optical/IR, then the temporal decay rates would only match a post jet-break model, with electron index p Copyright © 2023, The Authors. All rights reserved.

关键词： Supernovae

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Highly Reversible Zinc Metal Anode in a Dilute Aqueous Electrolyte Enabled by a pH Buffer Additive

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Angewandte Chemie 2022年第5期135卷

作者： Wei Zhang Yuhang Dai Ruwei Chen Zhenming Xu Jianwei Li Wei Zong Huangxu Li Zheng Li Zhenyu Zhang Jiexin Zhu Fei Guo Xuan Gao Zijuan Du Jintao Chen Tianlei Wang Guanjie He Ivan P. Parkin Christopher Ingold Laboratory Department of Chemistry University College London London WC1H 0AJ UK Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Writing - original draft (lead) Electrochemical Innovation Lab (EIL) Department of Chemical Engineering University College London London WC1E 7JE UK Contribution: Conceptualization (supporting) Investigation (supporting) Methodology (supporting) Writing - review & editing (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Writing - review & editing (supporting) Jiangsu Key Laboratory of Electrochemical Energy Storage Technologies College of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China Contribution: Data curation (supporting) Investigation (supporting) Methodology (supporting) Writing - review & editing (supporting) Contribution: Investigation (supporting) Methodology (supporting) Writing - review & editing (supporting) Department of Chemistry City University of Hong Kong Kowloon Hong Kong 999077 P. R. China School of Metallurgy and Environment Central South University Changsha 410083 P. R. China Contribution: Investigation (supporting) Methodology (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Writing - review & editing (supporting) Contribution: Investigation (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Funding acquisition (equal) Supervision (lead) Writing - review & editing (lead) Contribution: Funding acquisition (lead) Supervision (supporting) Writing - review & editing (supporting)

Aqueous zinc-ion batteries have drawn increasing attention due to the intrinsic safety, cost-effectiveness and high energy density. However, parasitic reactions and non-uniform dendrite growth on the Zn anode side impede their application. Herein, a multifunctional additive, ammonium dihydrogen phosphate (NHP), is introduced to regulate uniform zinc deposition and to suppress side reactions. The results show that the NH 4 + tends to be preferably absorbed on the Zn surface to form a “shielding effect” and blocks the direct contact of water with Zn. Moreover, NH 4 + and (H 2 PO 4 ) − jointly maintain pH values of the electrode-electrolyte interface. Consequently, the NHP additive enables highly reversible Zn plating/stripping behaviors in Zn//Zn and Zn//Cu cells. Furthermore, the electrochemical performances of Zn//MnO 2 full cells and Zn//active carbon (AC) capacitors are improved. This work provides an efficient and general strategy for modifying Zn plating/stripping behaviors and suppressing side reactions in mild aqueous electrolyte.

关键词： Additive Aqueous Electrolyte Dendrite Growth Zinc Anode pH Buffer

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Intensifying a semiarid wheat monocropping with forage rape as a replacement to fallow period in China

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Crop science 2020年第2期61卷

作者： Jianqiang Deng Zhixin Zhang Hong Ni Xingfa Lai Samaila Usman Qian Yang Xianlong Yang Jeffrey A. Coulter Yuying Shen State Key Lab. of Grassland Agro-ecosystems of Lanzhou Univ. Lanzhou 730020 China College of Pastoral Agriculture Science and Technology Lanzhou Univ. Lanzhou 730020 China Key Lab. of Grassland Livestock Industry Innovation Ministry of Agriculture and Rural Affairs Lanzhou Univ. Lanzhou 730020 China Contribution: Conceptualization Data curation Funding acquisition Investigation Project administration Resources Supervision Validation Writing - review & editing College of Grassland Agriculture Northwest A & F Univ. Yangling Shaanxi 712100 China Dep. of Agronomy and Plant Genetics Univ. of Minnesota St. Paul MN 55108 USA National Demonstration Center for Experimental Grassland Science Education Lanzhou Univ. Lanzhou 730020 China

The traditional cropping system on the Loess Plateau of China is winter wheat ( Triticum aestivum L., W) monocropping. However, this cropping system, with a 3-mo summer fallow (F) period, has exhibited low radiation and precipitation use efficiency. The objectives of this study conducted in 2016–2018 was to investigate system productivity and resource (i.e., water and radiation) use efficiency when winter wheat monocropping was intensified by planting forage rape ( Brassica napus L., R) during the summer fallow period. Dry matter yield (DM), water use efficiency (WUE), and radiation use efficiency (RUE) of forage rape and winter wheat in F-W and R-W crop rotations were determined. The results show that forage rape did not change system DM in all years, and winter wheat grain yield as well as DM in 2018, but decreased winter wheat grain yield and DM by 17 and 16% in 2017, respectively. Forage rape increased the system average water use (WU) by 20%, whereas system WUE of DM (WUE DM ) was unaffected in both years. Forage rape also improved system intercepted photosynthetically active radiation (IPAR) by 39% but did not alter winter wheat IPAR in both years. In 2017, intensification with forage rape decreased winter wheat RUE DM and RUE of grain (RUE Grain ). In conclusion, crop productivity was mainly related to water availability during sowing and the growing season, whereas RUE was related to crop productivity. Therefore, soil water supply needs to be considered before sowing forage rape during the summer fallow in the semiarid region of China as examined in this study.

关键词：

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DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

arXiv

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arXiv 2017年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Xu, Jingfang Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Sogou Inc Beijing China

This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected;2) local relevances are determined;3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Information retrieval

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A deep relevance matching model for ad-hoc retrieval

arXiv

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arXiv 2017年

作者： Guo, Jiafeng Fan, Yixing Ai, Qingyao Bruce Croft, W. CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Center for Intelligent Information Retrieval University of Massachusetts Amherst MA United States

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models. Copyright © 2017, The Authors. All rights reserved.

关键词： Natural language processing systems

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Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

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International Journal of Digital Earth 2017年第7期10卷 687-700页

作者： Yongxian Su Xiuzhi Chen Hua Su Liyang Liu Jishan Liao Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangzhouPeople’s Republic of China Jiangsu Key Laboratory of Agricultural Meteorology College of Applied MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhouPeople’s Republic of China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National Engineering Research Centre of Geo-spatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital ***,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer *** using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),*** results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture *** corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),*** monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),***,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.

关键词： Surface soil moisture land surface temperature physical-based radiative transfer model AMSR-E brightness temperatures

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A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification 1st

A Novel Intrusion Detection System Based on Advanced Naive B...

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1st International Conference on 5G for Future Wireless networks, 5GWN 2017

作者： Wang, Yunpeng Li, Yuzhou Tian, Daxin Wang, Congyu Wang, Wenyang Hui, Rong Guo, Peng Zhang, Haijun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University XueYuan Road No. 37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No. 68 Tianjin300300 China School of Computer and Communication Engineering University of Science and Technology Beijing XueYuan Road No. 30 Beijing100083 China XueYuan Road No. 37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China Key Lab of Urban ITS Technology Optimization and Integration The Ministry of Public Security of China Hefei230088 China

ISBN: (纸本)9783319728223

Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance. © 2018, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Intrusion detection

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Multi-task representation learning for demographic prediction 38th

Multi-task representation learning for demographic predictio...

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38th European Conference on Information Retrieval Research, ECIR 2016

作者： Wang, Pengfei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China

ISBN: (纸本)9783319306704

Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require professional knowledge and often suffer from under specification. Meanwhile, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. In this paper, we propose a novel Multi-task Representation Learning (MTRL) model to predict users’ demographic attributes. Comparing with the previous methods, our model conveys the following merits: (1) By using a multitask approach to learn the tasks, our model leverages the large amounts of cross-task data, which is helpful to the task with limited data;(2) MTRL uses a supervised way to learn the shared semantic representation across multiple tasks, thus it can obtain a more general and robust representation by considering the constraints among tasks. Experiments are conducted on a real-world retail dataset where three attributes (gender, marital status, and education background) are predicted. The empirical results show that our MTRL model can improve the performance significantly compared with the state-of-the-art baselines. © Springer International Publishing Switzerland 2016.

关键词： Semantics

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