Classes Of Events Research Articles

Dose-response, efficacy, and safety of oral semaglutide monotherapy in Japanese patients with type 2 diabetes (PIONEER 9): a 52-week, phase 2/3a, randomised, controlled trial

Given the unique phenotype of type 2 diabetes in Japanese patients, novel therapies such as oral semaglutide require evaluation in this population. PIONEER 9 aimed to assess the dose-response of oral semaglutide and to compare the efficacy and safety of oral semaglutide with placebo and a subcutaneous GLP-1 receptor agonist in a Japanese population. PIONEER 9 was a 52-week, phase 2/3a, randomised, controlled trial done at 16 sites (clinics and university hospitals) in Japan. Japanese patients aged 20 years or older with uncontrolled type 2 diabetes managed by diet or exercise or with oral glucose-lowering drug monotherapy (washed out) were randomly assigned (1:1:1:1:1) to receive double-blind once-daily oral semaglutide (3 mg, 7 mg, or 14 mg) or placebo, or open-label subcutaneous once-daily liraglutide 0·9 mg. The primary endpoint was change in HbA1c from baseline to week 26 with the trial product (primary) estimand (which assumes all patients remained on trial product without rescue medication use) in all randomly assigned patients. This trial is registered with ClinicalTrials.gov, NCT03018028. Between Jan 10, and July 11, 2017, 243 patients were randomly assigned to oral semaglutide 3 mg (n=49), 7 mg (n=49), or 14 mg (n=48), or placebo (n=49), or to liraglutide 0·9 mg (n=48). Changes in HbA1c from baseline (mean 8·2%) to week 26 were dose-dependent with oral semaglutide (mean change -1·1% [SE 0·1] for oral semaglutide 3 mg, -1·5% [0·1] for 7 mg, and -1·7% [0·1] for 14 mg), -0·1% (0·1) with placebo, and -1·4% (0·1) with liraglutide 0·9 mg. Estimated treatment differences for change in HbA1c compared with placebo were -1·1 percentage points (95% CI -1·4 to -0·8; p<0·0001) for oral semaglutide 3 mg, -1·5 percentage points (-1·7 to -1·2; p<0·0001) for oral semaglutide 7 mg, and -1·7 percentage points (-2·0 to -1·4; p<0·0001) for oral semaglutide 14 mg. Estimated treatment differences for change in HbA1c compared with liraglutide 0·9 mg were 0·3 percentage points (95% CI -0·0 to 0·6; p=0·0799) for oral semaglutide 3 mg, -0·1 percentage points (-0·4 to 0·2; p=0·3942) for oral semaglutide 7 mg, and -0·3 percentage points (-0·6 to -0·0; p=0·0272) for oral semaglutide 14 mg. Gastrointestinal events, predominantly of mild or moderate severity, were the most frequently reported class of adverse event with oral semaglutide: constipation was most common, occurring in five to six (10-13%) patients with oral semaglutide, three (6%) with placebo, and nine (19%) with liraglutide 0·9 mg. This study showed that oral semaglutide provides significant reductions in HbA1c compared with placebo in a dose-dependent manner in Japanese patients with type 2 diabetes, and has a safety profile consistent with that of GLP-1 receptor agonists. Novo Nordisk.

A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports.

Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

A 3‐Year Sample of Almost 1,600 Elves Recorded Above South America by the Pierre Auger Cosmic‐Ray Observatory

AbstractElves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms. We report the observation of 1,598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the fluorescence detector (FD) of the Pierre Auger Cosmic‐Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of km , reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultrahigh energy cosmic‐ray observations, the Auger FD turns out to be very sensitive to the ultraviolet emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year‐round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18% of the elve events have more than one peak, compatible with intracloud activity. Within the 3‐year sample, 72% of the elves correlate with the far‐field radiation measurements of the World Wide Lightning Location Network. The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year‐round operation and full horizon coverage.

Road Data Enrichment Framework Based on Heterogeneous Data Fusion for ITS

In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location-Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS’ routes against Google Maps’ routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Information (RI), and Area Tags (AT), to enrich T-MAPS’ suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user’s viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).

Mutating City: Designing Events as a Matter of Social Innovation

Contemporary European cities have become so large and complex that is extremely difficult, especially in times of economic crisis, to set up for them a top-down type of project. Cities can respond in a more measured way to the inevitable ongoing change by continuously mutating, in a process of collective awareness about their real-time status. This position was built up through a plethora of bottom-up, shared actions that are co-designed and implemented, and belong to a process that encompasses also what is transient and temporary, what is ad-hoc and what relates to the digital sphere. Faced with this challenging backdrop, the subject matter of design comes into play and, by operating on services, events and analogical and digital communications, it sets itself up as a discipline that can mediate between the need to govern continuous urban mutation and the way in which this proposal can take an effective and appropriate shape. In this article, the authors take stock of the role of design within the processes of mutation that are occurring within several large urban areas in Italy, in the cities where a culture of design is prevailing over grand projects of modern-day “urban mysticism”. This study will closely examine the class of events that form a process rather than a product. As such, they can adapt easily to a logic of continuous mutation that is feather-light, sustainable and engenders participation, both when design is their pretext and when design is the instrument to give events substance and a tangible form.

ESeismic: Towards an Ecuadorian volcano seismic repository

In this work, we present the development, description, and performance evaluation of two volcano seismological datasets: one containing raw seismic signals (MicSigV1) and another containing a set of 84 distinctive attributes or features for bench-marking (SeisBenchV1), which was extracted from the MicSigV1 dataset, recorded at Cotopaxi volcano in Ecuador. These datasets represent the first public Ecuadorian volcano seismicity repository that will update, publish, and support the release of data to the scientific community on a continual basis. Each dataset contains samples of mostly long-period and volcano-tectonic event classes. Other types of events, such as regional, hybrid, and icequakes, were also included despite their minor presence in the datasets. These datasets will serve to provide an important resource for research in signal processing, feature calculation, feature selection, feature space reduction, seismic events detection, and classification areas. The datasets can be downloaded through a copyright agreement that supports the correct use of the released datasets.

Take the Right Turn: The Role of Social Signals and Action\u2013Reaction Sequences in Enacting Turning Points in Negotiations

Negotiations and conflicts do not evolve smoothly but are discontinuous involving transitions, break-, and turning points that change the flow of the negotiation. Given that these departures may be decisive in determining whether the involved parties come to a successful conclusion, several scholars have pointed out the importance of investigating whether impasse and settlement dyads exhibit different turning point profiles. To address this question, we extended Druckman’s (J Confl Resolut 45:519–544, 2001) turning point model by integrating interlocking action–reaction sequences that initiate and (dis)confirm the departure from zero-sum bargaining. Furthermore, we consider social signals as previously not addressed class of events triggering the turning point. We propose and show that social signals act as precipitants to substantive change at the offer level and that how negotiators enact the action–reaction sequences discriminates between successful and unsuccessful dyads.

Tsunami earthquakes: Vertical pop-up expulsion at the forefront of subduction megathrust

Published slip distribution models, based on geodetic, seismological and tsunami data, of the Mw 7.8, 2010 Mentawai tsunami earthquake offshore south-central Sumatra, suggest that the large tsunami wave was generated by a narrow swath of high seafloor uplift along the accretionary wedge front, implying higher vertical throw than that consistent with slip on the shallow-dipping megathrust. Here we present high-resolution seismic reflection profiles across the 2010 rupture zone that image the youngest deformation at the accretionary wedge front. The profiles reveal conjugate, steeply-dipping, active thrust faults that branch upwards from the megathrust and bound triangular pop-ups. The seismologically determined co-seismic slip (≥10 m) on the 6°-dipping decollement probably caused a comparable amount of upward expulsion of these ∼3 km–wide, flat-topped pop-ups. Co-seismic throw on the ≈60° dipping thrusts that bound the pop-up plateaus maximize the uplift of the seafloor and overlying water-column, providing an additional localised tsunami source. Tsunami simulations show that such combined deformation, i.e. the broad-scale seafloor displacement caused by slip on the megathrust and the localized 8–10 m seafloor uplift across a 6–9 km-wide pop-up belt involving up to three pop-ups, is able to reproduce the 2010 tsunami amplitude measured by a DART buoy, and observed run-up heights in the Mentawai Islands. This simple mechanism, observed in analogue sandbox shortening experiments, may thus efficiently generate the oversize waves that characterize Tsunami-Earthquakes. Systematic mapping of pop-ups along accretionary wedge fronts may help identify trench segments prone to produce the special class of seismic events that spawn exceptionally large tsunamis.

Determination of the Effective Local Lethal Function for the NanOx Model.

NanOx is a biophysical model recently developed in the context of hadrontherapy to predict the cell survival probability from ionizing radiation. It postulates that this may be factorized into two independent terms describing the cell response to two classes of biological events that occur in the sequence of an irradiation: the local lethal events that occur at nanometric scale and can by themselves induce cell death, and the non-local lethal events that lead to cell death by an effect of accumulation and/or interaction at a larger scale. Here we address how local lethal events are modeled in terms of the inactivation of undifferentiated nanometric targets via an "effective local lethal function F", which characterizes the response of each cell line to the spectra of "restricted specific energy". F is initially determined as a linear combination of basis functions. Then, a parametric expression is used to reproduce the function's main features, a threshold and a saturation, while at the same time reducing the number of free parameters. This strategy was applied to three cell lines in response to ions of different type and energy, which allows for benchmarking of the α(LET) curves predicted with both effective local lethal functions against the experimental data.

Measurement of electrons from heavy-flavour hadron decays as a function of multiplicity in p-Pb collisions at sqrt{s_{mathrm{NN}}} = 5.02 TeV

The multiplicity dependence of electron production from heavy-flavour hadron decays as a function of transverse momentum was measured in p-Pb collisions at sqrt{s_{mathrm{NN}}} = 5.02 TeV using the ALICE detector at the LHC. The measurement was performed in the centre-of-mass rapidity interval −1.07 < ycms< 0.14 and transverse momentum interval 2 < pT< 16 GeV/c. The multiplicity dependence of the production of electrons from heavy-flavour hadron decays was studied by comparing the pT spectra measured for different multiplicity classes with those measured in pp collisions (QpPb) and in peripheral p-Pb collisions (Qcp). The QpPb results obtained are consistent with unity within uncertainties in the measured pT interval and event classes. This indicates that heavy-flavour decay electron production is consistent with binary scaling and independent of the geometry of the collision system. Additionally, the results suggest that cold nuclear matter effects are negligible within uncertainties, in the production of heavy-flavour decay electrons at midrapidity in p-Pb collisions.

Inflammation-Induced Alternative Pre-mRNA Splicing in Mouse Alveolar Macrophages.

Alveolar macrophages serve as central orchestrators of inflammatory responses in the lungs, both initiating their onset and promoting their resolution. However, the mechanisms that program macrophages for these dynamic responses are not fully understood. Over 95% of all mammalian genes undergo alternative pre-mRNA splicing. While alternative splicing has been shown to regulate inflammatory responses in macrophages in vitro, it has not been investigated on a genome-wide scale in vivo. Here we used RNAseq to investigate alternative pre-mRNA splicing in alveolar macrophages isolated from lipopolysaccharide (LPS)-treated mice during the peak of inflammation and during its resolution. We found that lung inflammation induced substantial alternative pre-mRNA splicing in alveolar macrophages. The number of changes in isoform usage was greatest at the peak of inflammation and involved multiple classes of alternative pre-mRNA splicing events. Comparative pathway analysis of inflammation-induced changes in alternative pre-mRNA splicing and differential gene expression revealed overlap of pathways enriched for immune responses such as chemokine signaling and cellular metabolism. Moreover, alternative pre-mRNA splicing of genes in metabolic pathways differed in tissue resident vs. recruited (blood monocyte-derived) alveolar macrophages and corresponded to changes in core metabolism, including a switch to Warburg-like metabolism in recruited macrophages with increased glycolysis and decreased flux through the tricarboxylic acid cycle.

ANALISIS STRUKTUR YANG TERKANDUNG DALAM CERPEN UMAH

Literary works are creations communicated expressively about the author`s intent for aesthetic purposes.. This study examines the intrinsic contained in the short story of Bali language entitled “umah” generally have opinions and different interpretation of a short story. The intrinsic element is all the outside elements that underlies and creation of literary works. The method used in this research descriptive research using descriptive research method. Method is a methodthe status of a human group, an object, a set of conditions, a system ofthought, or a classof events in the present. In the analysis of the short stories in Bali language, the work of this I Dewa Ketut Raka Kusuma, contains many moral values and social value that can be taken and learned by the readers.

Evaluation of Ground-Motion Models for U.S. Geological Survey Seismic Hazard Forecasts: Hawaii Tectonic Earthquakes and Volcanic Eruptions

ABSTRACTThe selection and weighting of ground-motion models (GMMs) introduces a significant source of uncertainty in U.S. Geological Survey (USGS) National Seismic Hazard Modeling Project (NSHMP) forecasts. In this study, we evaluate 18 candidate GMMs using instrumental ground-motion observations of horizontal peak ground acceleration (PGA) and 5%-damped pseudospectral acceleration (0.02–10 s) for tectonic earthquakes and volcanic eruptions, to inform logic-tree weights for the update of the USGS seismic hazard model for Hawaii. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function (PDF), mean and standard deviations σ, of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Hawaii seismic hazard model logic trees. The total residual PDF approach provides additional information by preserving GMM over- and underprediction across a broad spectrum of periods that is not available from a single value LLH score. We apply these GMM evaluation methods to two different data sets: (1) a database of instrumental ground motions from historic earthquakes in Hawaii from 1973 to 2007 (Mw 4–7.3) and (2) available ground motions from recent earthquakes (Mw 4–6.9) associated with 2018 Kilauea eruptions. The 2018 Kilauea sequence contains both volcanic eruptions and tectonic earthquakes allowing for statistically significant GMM comparisons of the two event classes. The Kilauea ground observations provide an independent data set allowing us to evaluate the predictive power of GMMs implemented in the new USGS nshmp-haz software system. We evaluate GMM performance as a function of earthquake depth and we demonstrate that short-period volcanic eruption ground motions are not well predicted by any candidate GMMs. Nine of the initial 18 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the update of the Hawaii seismic hazard model. A weighted mean of four top performing GMMs in this study (NGAsubslab, NGAsubinter, ASK14, A10) is 50% lower for PGA than for GMMS used in the previous USGS seismic hazard model for Hawaii.

Discriminant Analysis for Radar Signal Classification

Discriminant analysis is a technique used in statistics and machine learning to separate two or more classes of objects or events. We introduce linear, quadratic, and mixture discriminant analysis methods into radar signal classification. However, the selection of an appropriate discriminant analysis method can be difficult and no comparison study of these discriminant analyses for radar signal classification can be found in the open literature. This article presents a theoretical analysis and practical comparison of these three discriminant analysis methods for radar signal classification. The theoretical analysis is derived from Bayes' theorem. The practical comparison study is performed on a dataset consisting of five radars emissions. The advantages and drawbacks of each discriminant analysis method are highlighted. This study demonstrates that quadratic discriminant analysis (QDA) is predominantly a better method for radar signal classification using our radar dataset. On average, it demonstrates 95%, 93%, and 86.6% classification accuracy for three, four, and five radar emitters in our radar dataset, respectively. Linear discriminant analysis (LDA) achieves on average 88.7%, 84.9%, and 79.2% classification accuracy for three, four, and five radar emitters, respectively. Mixture discriminant analysis (MDA) also achieves the same classification performance as QDA. Theoretical analysis shows that both LDA and QDA are a special case of MDA and that MDA can set up more decision boundaries than LDA and QDA if the feature distribution in the dataset is an ensemble of Gaussian distributions. Therefore, MDA is recommended for radar signal classification.

Neo-Davidsonian-Based Event Class Semantic Representation

Abstract Event class is an abstract event that represents a set of events with some common features. There are inherent relations among event classes. Event classes and their relations are the important parts of event knowledge base. We gave a novel framework to represent event class semantic by marriage Neo-Davidsonian event semantic and 6-element event class model. The framework treated the predicate of event class as unary predicate with event class argument ec only. It connected the predicate and the other elements of event class with connecting symbol ⁁. And the framework can’t only represent the taxonomic relations among event classes, but also respectively represent non-taxonomic relations via the connecting symbols ⊄, ⊳, →, ∥ and ⊒. The representation examples indicate the novel framework can represent event classes semantic and different kinds of relations among event classes.

Fluctuations of anisotropic flow in Pb+Pb collisions at sqrt{{mathrm{s}}_{mathrm{NN}}} = 5.02 TeV with the ATLAS detector

Multi-particle azimuthal cumulants are measured as a function of centrality and transverse momentum using 470 μb−1 of Pb+Pb collisions at sqrt{s_{mathrm{NN}}} = 5.02 TeV with the ATLAS detector at the LHC. These cumulants provide information on the event-by-event fluctuations of harmonic flow coefficients vn and correlated fluctuations between two harmonics vn and vm. For the first time, a non-zero four-particle cumulant is observed for dipolar flow, v1. The four-particle cumulants for elliptic flow, v2, and triangular flow, v3, exhibit a strong centrality dependence and change sign in ultra-central collisions. This sign change is consistent with significant non-Gaussian fluctuations in v2 and v3. The four-particle cumulant for quadrangular flow, v4, is found to change sign in mid-central collisions. Correlations between two harmonics are studied with three- and four-particle mixed-harmonic cumulants, which indicate an anti-correlation between v2 and v3, and a positive correlation between v2 and v4. These correlations decrease in strength towards central collisions and either approach zero or change sign in ultra-central collisions. To investigate the possible flow fluctuations arising from intrinsic centrality or volume fluctuations, the results are compared between two different event classes used for centrality definitions. In peripheral and mid-central collisions where the cumulant signals are large, only small differences are observed. In ultra-central collisions, the differences are much larger and transverse momentum dependent. These results provide new information to disentangle flow fluctuations from the initial and final states, as well as new insights on the influence of centrality fluctuations.

Sound Event Detection Using Multiple Optimized Kernels

Sound event detection (SED) has been widely applied in real world applications. Convolutional recurrent neural network based SED approaches have achieved state-of-the-art performance. However, the convolution process is typically performed by using a fixed sized kernel, which adversely affects the detection accuracy especially when the acoustic features of different event classes are characterized by high variations. To deal with this, this article proposes a sound event detection technique using a convolutional recurrent neural network framework with multiple convolutional kernels of different sizes. The top performing kernels are selected from a kernel pool based on the unsupervised clustering errors and the accuracies of the temporarily trained models. Afterwards, the selected kernels are fed to multiple convolution layers to deal with the acoustic feature variations. Experimental results on different subsets of AudioSet, namely the DCASE Challenge 2017 Task 4 and DCASE Challenge 2018 Task 4, demonstrate the performance of the proposed approach compared to state-of-the-art systems.

Evolution of the ROOT Tree I/O

The ROOT TTree data format encodes hundreds of petabytes of High Energy and Nuclear Physics events. Its columnar layout drives rapid analyses, as only those parts (“branches”) that are really used in a given analysis need to be read from storage. Its unique feature is the seamless C++ integration, which allows users to directly store their event classes without explicitly defining data schemas. In this contribution, we present the status and plans of the future ROOT 7 event I/O. Along with the ROOT 7 interface modernization, we aim for robust, where possible compile-time safe C++ interfaces to read and write event data. On the performance side, we show first benchmarks using ROOT’s new experimental I/O subsystem that combines the best of TTrees with recent advances in columnar data formats. A core ingredient is a strong separation of the high-level logical data layout (C++ classes) from the low-level physical data layout (storage backed nested vectors of simple types). We show how the new, optimized physical data layout speeds up serialization and deserialization and facilitates parallel, vectorized and bulk operations. This lets ROOT I/O run optimally on the upcoming ultra-fast NVRAM storage devices, as well as file-less storage systems such as object stores.

Framework for rare event detection using Artificial Neural Network based context free grammar

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Topic Detection and Tracking Based on Event Ontology

In recent years, Topic Detection and Tracking (TDT) has served as a core technology for searching, organizing and structuring news oriented textual materials from a variety of internet news and social media. The biggest challenges of TDT are the sparsity and complexity of data, organization of topic granularity, unexpectedness of emergency topics, and the unpredictability of topics evolution. This paper proposes a new TDT method based on event ontology for hierarchical topic detection and tracking topic evolution, named TDTEO. As domain-oriented event knowledge base, the event ontology provides event classes hierarchy based on domain common sense, as well a set of scenario models that describe the occurrence and evolution of different types of emergency events. The proposed method solves the problem that new emerging events are easy to be missed in the process of topic detection, and solves the problem that the topic model is easy to result in semantics drift due to the dynamic evolution of topic, and it can effectively improve the accuracy of topic detection and tracking. Experiments show our models achieve satisfactory performance of topic detection with a maximum macro-F1 value of 85.25%, and the (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Det</sub> )Norm of topic tracking in the datasets is as low as 0.1028. Experimental results show that hierachical topic model can effectively detect the topics and tracking model based on event scenario can reflect the trend of topic evolution.