Time Distribution Of Events Research Articles

Comprehensive Evaluation of Shale Reservoir Reconstruction based on Microseismic and Multidisciplinary Integration

Microseismic can build a bridge between engineering operations such as drilling and fracturing and stratum evaluation such as earthquake, geology, and logging by fully excavating fracture time, spatial development characteristics, and focal information. For the postseismic evaluation of microseisms, a comprehensive evaluation system integrating microseisms and multidisciplines is established in this paper: through deep excavation of microseismic information such as the time and space distribution of microseismic events, quantitative statistics, magnitude-frequency gradient ([Formula: see text]-value) and S-P wave energy ratio (Es/Ep), the effective identification of dry faults, wet faults, cracks, joints, sweet spots and nonsweet spots is realized, combined with seismology and geology. The engineering problems (casing change, pressure change, fracturing barrier, etc.) are analyzed accordingly, which enhances the comprehensive evaluation function of microseismic and multidiscipline.

Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin.

Statistical analysis of longitudinal data often involves modeling treatment effects on clinically relevant longitudinal biomarkers since an initial event (the time origin). In some studies including preventive HIV vaccine efficacy trials, some participants have biomarkers measured starting at the time origin, whereas others have biomarkers measured starting later with the time origin unknown. The semiparametric additive time-varying coefficient model is investigated where the effects of some covariates vary nonparametrically with time while the effects of others remain constant. Weighted profile least squares estimators coupled with kernel smoothing are developed. The method uses the expectation maximization approach to deal with the censored time origin. The Kaplan-Meier estimator and other failure time regression models such as the Cox model can be utilized to estimate the distribution and the conditional distribution of left censored event time related to the censored time origin. Asymptotic properties of the parametric and nonparametric estimators and consistent asymptotic variance estimators are derived. A two-stage estimation procedure for choosing weight is proposed to improve estimation efficiency. Numerical simulations are conducted to examine finite sample properties of the proposed estimators. The simulation results show that the theory and methods work well. The efficiency gain of the two-stage estimation procedure depends on the distribution of the longitudinal error processes. The method is applied to analyze data from the Merck 023/HVTN 502 Step HIV vaccinestudy.

On the Time Distribution of Supernova Antineutrino Flux

Neutrino leptonic flavor symmetry violation is the only evidence for physics beyond the standard model. Much of what we have learned on these particles is derived from the study of their natural sources, such as the Sun or core-collapse supernovae. Neutrino emission from supernovae is particularly interesting and leptonic flavor transformations in supernova neutrinos have attracted a lot of theoretical attention. Unfortunately, the emission of core-collapse supernovae is not fully understood: thus, an inescapable preliminary step to progress is to improve on that, and future neutrino observations can help. One pressing and answerable question concerns the time distribution of the supernova anti-neutrino events. We propose a class of models of the time distribution that describe emission curves similar to those theoretically expected and consistent with available observations from the data of supernova SN1987A. They have the advantages of being motivated on physical bases and easy to interpret; they are flexible and adaptable to the results of the observations from a future galactic supernova. Important general characteristics of these models are the presence of an initial ramp and that a significant portion of the signal is in the first second of the emission.

Abstract 102: Clinicopathological and molecular characteristics, treatment patterns, and outcomes in patients with KRAS p.G12C-mutated metastatic non-small cell lung cancer (NSCLC) in the AACR Project GENIE database

Abstract Introduction: Mutations in the RAS family of proto-oncogenes are frequently found in NSCLC, with KRAS being the most prevalent mutated isoform. Of KRAS mutations, the most common is KRAS G12C, representing ~40% of KRAS mutations and occurring in ~13% of all lung adenocarcinoma cases. The objective of this study is to describe clinicopathological and molecular characteristics, treatment patterns, and outcomes in patients with KRAS G12C-mutated metastatic NSCLC using the AACR Project GENIE database. Methods: An observational retrospective study of adult patients (>18 years of age) with KRAS G12C-mutated metastatic NSCLC from three US academic comprehensive cancer centers in the AACR Project GENIE database was undertaken using de-identified data. Patients were eligible if their tumor tested positive for KRAS G12C mutation by next-generation sequencing at the participating institutions. Event-time distributions were estimated using the Kaplan-Meier method, and multivariable models were fitted to estimate adjusted hazard ratios (aHRs) by Cox proportional models. Patients with large panel sequencing performed up to Dec 31, 2018 were included. All available data until Aug 1, 2019 were collected. Results: Of the 416 eligible patients included in this analysis, 91.6% were white, 64.4% were female, median age at metastatic diagnosis was 68.2 years (range: 37.6-86.2), and 97.1% were former or current smokers. 65.1% of patients had stage 4 NSCLC at diagnosis, and most tumors were adenocarcinomas (85.8%). The KRAS G12C mutation was nearly mutually exclusive (≤ 1.0%) with other actionable driver mutations in EGFR, ALK, ROS1, and BRAF but was co-occurred with STK11 (23.6%) and KEAP1 (9.9%) mutations. Among patients who received PD-(L)1 monotherapy (n=110), median progression-free (mPFS) and overall survival (mOS) were 4.6 and 11.9 months, respectively, while among those who received chemotherapy only (n=133) mPFS and mOS were 4.1 and 12.1 months, respectively. When clinical outcomes were analyzed according to co-occurring genomic alterations, we found that KRAS/STK11 co-mutations were associated with significantly worse outcomes with immunotherapy (PFS: aHR 0.44, P=0.002; OS: aHR 0.42, P=0.003) but not with chemotherapy (PFS: aHR 0.82, P=0.44; OS: aHR 0.81, P=0.43). There was no significant impact of co-occurring KRAS mutations with KEAP1, SMARCA4, ATM, RBM10 and TP53 on clinical outcomes to either PD-(L)1 inhibition or chemotherapy. Conclusions: KRAS G12C mutant NSCLC represents a heterogeneous group of lung cancers and its genomic diversity is reflected in differential responses to therapy. These findings highlight the urgent clinical need for molecular biomarker-driven therapies in this subset of patients. Citation Format: Biagio Ricciuti, Chenguang Wang, Hira Rizvi, Jacklynn Egger, Michele LeNoue-Newton, Marilyn Holt, Shivani Aggarwal, Huakang Tu, Hil Hsu, Xuena Wang, Gataree Ngarmchamnanrith, Victoria Chia, Jocelyn Lee, Robert Scharpf, Valsamo Anagnostou, Christine Lovly, Greg Riely, Mark Awad. Clinicopathological and molecular characteristics, treatment patterns, and outcomes in patients with KRAS p.G12C-mutated metastatic non-small cell lung cancer (NSCLC) in the AACR Project GENIE database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 102.

Information anchored reference‐based sensitivity analysis for truncated normal data with application to survival analysis

The primary analysis of time‐to‐event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post‐censoring insensitivity analyses. Reference‐based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference‐based sensitivity analysis with right censored log normal data isinformation anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study.

Prognostic effect of gender on outcome of treatment for adults with acute myeloid leukaemia.

There are conflicting reports in the literature suggesting that one gender or the other has a better survival with acute myeloid leukaemia (AML). The present study was done in an attempt to resolve the issue. The effect of gender was examined on 3546 newly diagnosed patients with AML, including 548 patients with acute promyelocytic leukaemia (APL) enrolled in 10 multi-institutional treatment studies from March 1984 to November 2008. Kaplan-Meier estimates were used to estimate event-time distributions for survival and multivariate models were used to examine the gender effect after adjusting for multiple risk factors. P values were based on two-sided tests. Non-APL female patients had a significantly better overall (OS) but not disease-free survival (DFS) than males, irrespective of age, initial white blood cell count, or dose of daunorubicin. No differences were observed for obese or FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD)-positive patients. Female patients with APL had a significantly better OS and DFS than male patients with APL, and differences in survival were greater for patients with t(15;17) + other cytogenetic abnormalities compared with those with t(15;17) only. Gender is an independent prognostic variable in patients with AML. Whether these survival differences are due to hormonal, genetic or pharmacokinetic differences between the sexes or differential toxin exposure such as smoking is unknown. However, the former seems less likely as patient age did not influence the survival advantage for female patients.

A Novel Prognostic Tool in Western and Eastern Biliary Tract Cancer Patients Treated in First-line Setting: the ECSIPOT Index.

The need to estimate prognosis of advanced BTC (aBTC) patients treated with first-line chemotherapy is compelling. The aim of the study is to evaluate the ECSIPOT (psECogSIiPnigOT) index, influenced by PECS (PsECogSii) index, prognostic nutritional index (PNI), and GOT. This international study was conducted on a training cohort of 126 patients and in three validation cohorts, both European and Korean. ECSIPOT index formula: (PECS:0 = 1 point; PECS:1 = 1.4 points; PECS:2 = 3.2 points) + (PNI > 36.7 = 1 point; PNI < 36.7 = 2 points) + (GOT < 100 = 1 point; GOT > 100 = 2 points). Event-time distributions were estimated using the Kaplan-Meier method, and survival curves were compared using the log-rank test. In the training cohort, mOS was 12.9, 6.3, and 2.8months for patients with ECSIPOT-0, ECSIPOT-1, and ECSIPOT-2, respectively (ECSIPOT-0: HR 1; ECSIPOT-1: HR 2.11; ECSIPOT-2: HR 4.93; p < 0.0001). In the first validation cohort, mOS was 11.5, 7.3, and 3.3months for ECSIPOT-0, ECSIPOT-1, and ECSIPOT-2, respectively (ECSIPOT-0: HR 1; ECSIPOT-1: HR 1.74; ECSIPOT-2: HR 3.41; p < 0.0001). In the second validation cohort, mOS was 25.2, 12.5, and 3.0months for ECSIPOT-0, ECSIPOT-1, and ECSIPOT-2, respectively (ECSIPOT-0: HR = 1; ECSIPOT-1: HR 2.33; ECSIPOT-2: HR 8.46; p < 0.0001). In the third validation cohort, mOS was 11.8, 8.1, and 4.6months for ECSIPOT-0, ECSIPOT-1, and ECSIPOT-2, respectively (ECSIPOT-0: HR = 1; ECSIPOT-1: HR 1.47; ECSIPOT-2: HR 3.17; p < 0.0001). Multivariate analysis in all cohorts confirmed the ECSIPOT index as an independent prognostic factor for OS. The easy assessment and good risk-stratification performance make the ECSIPOT index a promising tool to comprehensively estimate the prognosis of aBTC patients.

Thrombotic events in patients treated with immune checkpoint inhibitors for non-small cell lung cancer: A retrospective multicentric cohort study.

e21198 Background: Venous thromboembolism is a frequent complication of non-small cell lung cancer (NSCLC) and is associated with a worse prognosis, a reduced quality of life, and increased healthcare costs. Immune checkpoint inhibitors (ICI) are revolutionizing the management of NSCLC, but little is known about their impact on thrombosis. This study aims to define the incidence and clinical relevance of thrombosis in NSCLC patients receiving these treatments. Methods: A retrospective multicentric cohort study including 593 patients from three centers in Canada and France was performed. The cumulative incidence of venous thrombotic events after ICIs was calculated, and the impact of these events on survival and response to treatment was determined. Finally, univariate log-rank tests were performed to identify thrombosis risk factors. Results: The incidence of venous thrombosis in the cohort was 9.9% for an incidence rate of 76.5 thrombosis per 1000 person-years, with most thromboses occurring rapidly after treatment initiation. Thrombosis was not correlated with overall survival, progression-free survival, or objective response to ICIs (summarized in the table below). Age ˂ 65 years old (HR = 1.66; 95 % CI = 1.00 – 2.82) and a delay of less than 12 months from diagnosis to the first ICI treatment (HR = 1.74; 95 % CI = 1.03 – 2.87) were associated with an increased risk of thrombosis. Tumors with PD-L1 &gt; 1% were associated with more thrombosis in the first year since the beginning of therapy (HR = 3.06; 95 % CI = 1.19 – 4.76, p=0.015). Conclusions: This study suggests that the time distribution and incidence of thrombotic events in NSCLC patients treated with ICI are comparable to what is reported in other cohorts of patients treated with chemotherapy. In our cohort, thrombosis was not a prognostic factor for survival or response to therapy. Patient age &lt; 65 and tumors with PD-L1 &gt; 1% were associated to a higher risk of thrombotic events.[Table: see text]

BRCA1 Protein Expression Predicts Survival in Glioblastoma Patients from an NRG Oncology RTOG Cohort

Purpose: Glioblastoma, the most common malignant brain tumor, was associated with a median survival of <1 year in the pre-temozolomide (TMZ) era. Despite advances in molecular and genetic profiling studies identifying several predictive biomarkers, none has been translated into routine clinical use. Our aim was to investigate the prognostic significance of a panel of diverse cellular molecular markers of tumor formation and growth in an annotated glioblastoma tissue microarray (TMA). Methods and Materials: A TMA composed of archived glioblastoma tumors from patients treated with surgery, radiation, and non-TMZ chemother­apy, was provided by RTOG. RAD51, BRCA-1, phosphatase and tensin homolog tumor suppressor gene (PTEN), and miRNA-210 expression levels were assessed using quantitative in situ hybridization and automated quantitative protein analysis. The objectives of this analysis were to determine the association of each biomarker with overall survival (OS), using the Cox proportional hazard model. Event-time distributions were estimated using the Kaplan-Meier method and compared by the log-rank test. Results: A cohort of 66 patients was included in this study. Among the 4 biomarkers assessed, only BRCA1 expression had a statistically significant correlation with survival. From univariate analysis, patients with low BRCA1 protein expression showed a favorable outcome for OS (p = 0.04; hazard ratio = 0.56) in comparison with high expressors, with a median survival time of 18.9 versus 4.8 months. Conclusions: BRCA1 protein expression was an important survival predictor in our cohort of glioblastoma patients. This result may imply that low BRCA1 in the tumor and the consequent low level of DNA repair cause vulnerability of the cancer cells to treatment.

Valid and Approximately Valid Confidence Intervals for Current Status Data

Abstract We introduce a new approach for creating pointwise confidence intervals for the distribution of event times for current status data. Existing methods are based on asymptotics. Our approach is based on binomial properties and motivates confidence intervals that are very simple to apply and are valid that is guarantee nominal coverage. Although these confidence intervals are necessarily conservative for small sample sizes, asymptotically their coverage rate approaches the nominal one. This binomial approach also motivates approximately valid confidence intervals, and simulations show that these approximate intervals generally have coverage rates closer to the nominal level with shorter length than existing intervals, such as the confidence interval based on the likelihood ratio test. Unlike previous asymptotic methods that require different asymptotic distributions for continuous or grid-based assessment, the binomial approach can be applied to either type of assessment distribution.

Time Distribution of Strong Seismic Events in the Fore-Sudetic Monocline in Context of Signals Registered by Water-Tube Gauges in Książ Geodynamic Laboratory

Changes in the stress field in Świebodzice Depression (ŚU) unit area are the reason of complex kinematics of the rock blocks consisting of rotations and horizontal/vertical displacements. The measurement system of the Geodynamic Laboratory in Książ, associated with rock blocks which are separated by faults, is a natural detector of tectonic activity. Installed in laboratory long water-tube gauges allowing to determine the functions of tectonic activity—TAF, and their derivatives. A comparison of the TAF with the seismic activity of the Fore-Sudetic Monocline showed that the strong seismic shocks (magnitude ≥3.6) occur in the Monocline only during defined and repeatable phases of the kinematic activity of the ŚU. Observed concordance proves the thesis of the existence of a large-scale, and largely homogeneous field of tectonic forces which, at the same time, cover the ŚU and the Fore-Sudetic Monocline units. The results of comparison between seismic events temporal distribution and phases of tectonic activity of the ŚU orogen indicate existence of the time relation between function of derivative of the tectonic activity (TAF) and seismic events.

The time distribution of quantum events

We develop a general theory of the time distribution of quantum events, applicable to a large class of problems such as arrival time, dwell time and tunneling time. A stopwatch ticks until an awaited event is detected, at which time the stopwatch stops. The awaited event is represented by a projection operator π, while the ideal stopwatch is modeled as a series of projective measurements at which the quantum state gets projected with either π¯=1−π (when the awaited event does not happen) or π (when the awaited event eventually happens). In the approximation in which the time δt between the subsequent measurements is sufficiently small (but not zero!), we find a fairly simple general formula for the time distribution P(t), representing the probability density that the awaited event will be detected at time t.

Prognostic Role of a New Index Tested in European and Korean Advanced Biliary Tract Cancer Patients: the PECS Index.

The aim of the present study is to evaluate a new index (PECS (PsECogSii)index) influenced by PS ECOG and systemic immune-inflammation index (SII) in unresectable locally advanced or metastatic BTC patients treated with first-line chemotherapy. This multicenter, international, study was conducted on a training cohort of 130 patients and in three European and Korean validation cohorts The PECS index was calculated as ECOG × SII index (neutrophil count × platelet count/lymphocyte count). Event-time distributions were estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. In the training cohort, the median overall survival (mOS) was 13.2months, 8.7months, and 3.8months for patients with PECS-0, PECS-1, and PECS-2, respectively (PECS-0: HR=1; PECS-1: HR 1.41; PECS-2: HR 3.23) (p<0.0001). In the first validation cohort, the mOS was 12.8months, 10.1months, and 5.3months for patients with PECS-0, PECS-1, and PECS-2, respectively (PECS-0: HR=1; PECS-1: HR 1.29; PECS-2: HR 2.40) (p<0.0001). In the second validation cohort, the mOS was 21.2months, 10.2months, and 3.0months for patients with PECS-0, PECS-1, and PECS-2, respectively (PECS-0: HR=1; PECS-1: HR 2.25; PECS-2: HR 9.00) (p<0.0001). In the third validation cohort, the median OS was 15.5months, 7.5months, and 3.7months for patients with PECS-0, PECS-1, and PECS-2, respectively (PECS-0: ref HR=1; PECS-1: HR 2.14; PECS-2: HR 5.00) (p<0.0001). Multivariate analysis in all cohorts confirmed the PECS index as an independent prognostic factor for OS. The easy assessment, low cost, and reproducibility make PECS index a promising tool to assess the prognosis of BTC patients in future clinical practice.

Inference of Naturally Acquired Immunity Using a Self-matched Negative-Control Design.

Host adaptive immune responses may protect against infection or disease when a pathogen is repeatedly encountered. The hazard ratio of infection or disease, given previous infection, is typically sought to estimate the strength of protective immunity. However, variation in individual exposure or susceptibility to infection may introduce frailty bias, whereby a tendency for infections to recur among individuals with greater risk confounds the causal association between previous infection and susceptibility. We introduce a self-matched “case-only” inference method to control for unmeasured individual heterogeneity, making use of negative-control endpoints not attributable to the pathogen of interest. To control for confounding, this method compares event times for endpoints due to the pathogen of interest and negative-control endpoints during counterfactual risk periods, defined according to individuals’ infection history. We derive a standard Mantel-Haenszel (matched) odds ratio conveying the effect of prior infection on time to recurrence. We compare performance of this approach to several proportional hazards modeling frameworks and estimate statistical power of the proposed strategy under various conditions. In an example application, we use the proposed method to reestimate naturally acquired protection against rotavirus gastroenteritis using data from previously published cohort studies. This self-matched negative-control design may present a flexible alternative to existing approaches for analyzing naturally acquired immunity, as well as other exposures affecting the distribution of recurrent event times.

A multistate first-order Markov model for modeling time distribution of extreme rainfall events

The time distribution of extreme rainfall events is a significant property that governs the design of urban stormwater management structures. Accuracy in characterizing this behavior can significantly influence the design of hydraulic structures. Current methods used for this purpose either tend to be generic and hence sacrifice on accuracy or need a lot of model parameters and input data. In this study, a computationally efficient multistate first-order Markov model is proposed for use in characterizing the inherently stochastic nature of the dimensionless time distribution of extreme rainfall. The model was applied to bivariate extremes at 10 stations in India and 205 stations in the United States (US). A comprehensive performance evaluation was carried out with one-hundred stochastically generated extremes for each historically observed extreme rainfall event. The comparisons included: 1-h (15-min); 2-h (30-min); and, 3-h (45-min) peak rainfall intensities for India and (US) stations, respectively; number of first, second, third, and fourth-quartile storms; the dependence of peak rainfall intensity on total depth and duration; and, return levels and return periods of peak discharge when these extremes were applied on a hypothetical urban catchment. Results show that the model efficiently characterizes the time distribution of extremes with: Nash–Sutcliffe-Efficiency > 0.85 for peak rainfall intensity and peak discharge; < 20% error in reproducing different quartile storms; and, < 0.15 error in correlation analysis at all study locations. Hence the model can be used to effectively reproduce the time distribution of extreme rainfall events, thus increasing the confidence of design of urban stormwater management structures.

Effects of covariates on alternating recurrent events in accelerated failure time models.

In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

Spline-based accelerated failure time model.

The accelerated failure time (AFT) model has been suggested as an alternative to the Cox proportional hazards model. However, a parametric AFT model requires the specification of an appropriate distribution for the event time, which is often difficult to identify in real-life studies and may limit applications. A semiparametric AFT model was developed by Komárek et al based on smoothed error distribution that does not require such specification. In this article, we develop a spline-based AFT model that also does not require specification of the parametric family of event time distribution. The baseline hazard function is modeled by regression B-splines, allowing for the estimation of a variety of smooth and flexible shapes. In comprehensive simulations, we validate the performance of our approach and compare with the results from parametric AFT models and the approach of Komárek. Both the proposed spline-based AFT model and the approach of Komárek provided unbiased estimates of covariate effects and survival curves for a variety of scenarios in which the event time followed different distributions, including both simple and complex cases. Spline-based estimates of the baseline hazard showed also a satisfactory numerical stability. As expected, the baseline hazard and survival probabilities estimated by the misspecified parametric AFT models deviated from the truth. We illustrated the application of the proposed model in a study of colon cancer.

Time between Symptom Onset, Hospitalisation and Recovery or Death: Statistical Analysis of Belgian COVID-19 Patients.

There are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. We investigate the time from symptom onset to diagnosis and hospitalization or the length of stay (LoS) in the hospital, and whether there are differences in the population. Sciensano collected information on 14,618 hospitalized patients with COVID-19 admissions from 114 Belgian hospitals between 14 March and 12 June 2020. The distributions of different event times for different patient groups are estimated accounting for interval censoring and right truncation of the time intervals. The time between symptom onset and hospitalization or diagnosis are similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient (longest delay in age group 20–60 years) and whether or not the patient lives in a nursing home (additional 2 days for patients from nursing home). The median LoS in hospital varies between 3 and 10.4 days, with the LoS increasing with age. The hospital LoS for patients that recover is shorter for patients living in a nursing home, but the time to death is longer for these patients. Over the course of the first wave, the LoS has decreased.

Empower rumor events detection from Chinese microblogs with multi-type individual information

Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness of a rumor event, it will be verified and authenticated with multi-type individual information, especially with individuals’ emotional expressions to events and their own credibility. This paper presents a novel two-layer GRU model for rumor events detection based on multi-type individual information (MII) and a dynamic time-series (DTS) algorithm, named as MII–DTS-GRU. Specifically, MII refers to adopt the sentiment dictionary to identify fine-grained human emotional expressions to events and fuse with the individual credibility. Besides, the DTS algorithm retains the time distribution of social events. Experimental results on Sina Weibo datasets show that our model achieves a high accuracy of 96.3% and demonstrate that our proposed MII–DTS-GRU model outperforms the state-of-the-art models on rumor events detection.

Dark Matter Signals from Timing Spectra at Neutrino Experiments.

We propose a novel strategy to search for new physics in timing spectra at low-energy neutrino experiments using a pulsed beam, envisioning the situation in which a new particle comes from the decay of its heavier partner with a finite particle width. The timing distribution of events induced by the dark matter (DM) candidate particle scattering at the detector may populate in a relatively narrow range, forming a "resonancelike" shape. Because of this structural feature, the signal may be isolated from the backgrounds, in particular when the backgrounds are uniformly distributed in energy and time. For proof of the principle, we investigate the discovery potential for DM from the decay of a dark photon in the ongoing COHERENT experiment and show the exciting prospects for exploring the associated parameter space with this experiment. We analyze the existing CsI detector data with a timing cut and an energy cut, and we find, for the first time, an excess in the timing distribution that can be explained by such DM. We compare the sensitivity to the kinetic mixing parameter (ε) for current and future COHERENT experiments with the projected limits from LDMX and DUNE.