Changshanian Maximum Flooding Event in China and Its Global Correlation

Various orders of condensed sections are recognized in the Cambrian of North China Carbonate Platform. Study of comparative sedimentology proves that CS4-CS5 in the Changshanian age is the maximum sea flooding sediments in the Cambrian, regardless of the distribution, thickness, internal structure of the condensed section series and K2O abundance. It is a two-layered composite condensed section series, characterized by the enrichment of such elements as K, P, Mn, Ti, Co, V, Cr, Cu, Zr, Ni, Li, Th, La, Ce, Nd, Dy, Y, Sc and Be. The Changshanian maximum flooding event can be correlated on a global scale, and the corresponding sedimentary records are discovered in 26 intercontinental sections in N. Europe, N. America, and Tarim and the Yangtze Platform of China. Through detailed sedimentological research, meter-scale cycle analysis and Fischer plots, it is concluded that the Changshanian maximum flooding was a composite effect of the second-order eustacy superimposed by the third-and fourth-order eustacy, which lasted for 1.2-3 Ma. An intercontinental model of the Changshanian maximum flooding is proposed at the end of this paper.

Significant carbon isotope excursions in the Cambrian and their implications for global correlations

The existing δ 13C data in the Cambrian from different regions of the world are analyzed here. There are four well-documented carbon isotope excursions with global significance. In ascending order, they are： （1） a large negative excursion, comparable to ＂BACE＂ （BAsal Cambrian Carbon isotope Excursion） event, which occurs near the Precambrian-Cambrian boundary with a magnitude of 4‰-10‰ （PDB）; （2） the ＂ZHUCE＂ （ZHUjiaqing Carbon isotope Excursion） event, a distinct positive excursion （over 5%0） that can be recognized at the Fortunian Stage to Stage 2 transition; （3） another strong negative one, so-called ＂ROECE＂ （Redlichiid-Olenellid Extinction Carbon isotope Excursion） event, shifting at the interval between Series 2 and Series 3, peaking at -3‰--5‰ （PDB）; （4） the famous Steptoean positive carbon isotope excursion （SPICE）, which has been widely identified at the base of Furongian Series, Paibian Stage, with an amplitude about 4‰ （PDB）. The four sharp σ13C shifts correlate well with coeval paleoceanographic changes and bioevents. Besides, there are some σ13C excursions from a few sections in previous studies, and more data are required to identify whether they are global or regional ones.

Global correlation for strontium isotope curve in the Late Cretaceous of Tibet and dating marine sediments

^(87)Sr/^(86)Sr ratios of marine carbonate samples collected from a sedimentary section of the Late Cretaceous in the south of Tibet were measured.Based on the absence of cathodoluminescence and a very low Mn/Sr ratio(average 0.06)of the samples,it is thought that they contain information on the original seawater strontium isotope composition.The strontium isotope evolution curve of the Late Cretaceous in Tibet we established here,is consistent with other coeval curves from Europe,North America and Antarctica,supports the notion that the strontium isotope composition of seawater is governed by global events,which provides a new approach for the inter-continental and inter-basinal correlations of Late Cretaceous in the area and is a complementarity for biostratigraphy.In addition,we attempt to determine the age of the boundaries for Campanian/Santonian and Maastrichtian/Campanian by ^(87)Sr/^(86)Sr ratios for Gamba section in southern Tibet.The two boundaries are located in the thickness of 217 m(83.5 Ma)and 291 m(71.3 Ma),respectively.

Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

Application of Strontium Isotopic Stratigraphy to Dating Marine Sedimentary Units:A Case Study from the Permian Stratotype Section in Southern China

The calibration of sedimentary rock absolute dates is one of the difficulties in sedimentological and stratigraphic research.Since strontium(Sr)resides in seawater much longer(≈10^(6) a)than the seawater intermixing time(≈10^(3) a),the Sr isotopic composition of global seawater is uniform at any time and results in a stable system throughout geological history,based on which a global Sr isotope composition dating database has been established for age-calibration of marine strata.The Permian stratigraphic sections in the northern part of the Upper Yangtze block,southern China,record continuous marine sediments with clear stratigraphic boundaries and is suitable for stratigraphic dating of Sr isotopes.Based on sampling and Sr isotopic compositions of Permian carbonate strata in the northern part of the Upper Yangtze,a Permian Sr isotope evolution curve was established.According to the basic principles of Sr isotope stratigraphy,the global Strontium isotope age database can be used to calibrate the Permian stratigraphic dates in the northern Upper Yangtze.The results show that the Sr isotope evolution curves for the marine carbonate rocks in the Permian stratigraphic section of the Upper Yangtze present a decreasing trend from the mid-Qixia stage(P_(2))to the mid-Wujiaping stage(P_(3)),and then rise from the middle Wujiaping stage to the end of Changxing stage(P_(3)).When the Permian Sr-isotope evolution curve is compared with the global Sr isotope evolution curve in the northern Upper Yangtze,the two are consistent in their long-term evolutionary trend,indicating that Permian global geological events are important controlling factors for the composition and evolution of Sr isotopes.The 87 Sr/86 Sr value decreased gradually in the background of large-scale regressions at the turn of middle to late Permian period,revealing that the Emeishan basalt eruption occurred near the Maokou/Wujiaping boundary(GLB).Srisotope stratigraphy dating was performed on the boundaries of the Qixia Formation/Maokou Formation,Maokou Formation/Wujiaping Formation(GLB),Wujiaping Formation/Changxing Formation(WCB)and the Permian/Triassic(PTB)using the Global Strontium Isotope Age Database.The results are 270.4 Ma,261.2 Ma,254.5 Ma and 249.7 Ma,respectively.Based on this,the eruption age of the Emeishan basalts is defined at about 261.2 Ma.,which is more coincident with that acquired from other previous dating methods on the eruption age of the Emeishan basalts,and therefore proves that the application of Sr isotopic stratigraphy to dating marine sedimentary units is an effective method.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.