Confidence intervals for the ratio of Poisson parameters

Let k1, k2 be non-negative integers with k1 + k 2 ≥ 2; let X1, X 2,.. Xk1 ;Y1, Y2,.. Yk2 be mutually independent Poisson random variables with parameters lambda 1,lambda2,.. lk1 ; mu1, mu2,.. mk2 , respectively. In this article, we consider constructing confidence intervals for ratio of Poisson parameters q=l1˙l2 ˙l3&cdots;lk1 m1˙m2˙m 3&cdots;mk2 . For the ratio of two Poisson parameters q=l1m1 , an exact formula for confidence interval is given. A numerical method to obtain confidence interval for q=l1˙l2 ˙l3&cdots;lk1 m1˙m2˙m 3&cdots;mk2 is developed. Examples are given for each of the two cases

A Study of Joinpoint Models for Longitudinal Data

In many medical studies, data are collected simultaneously on multiple biomarkers from each individual. Levels of these biomarkers are measured periodically over certain time duration, giving rise to longitudinal trajectories. The subjects under study may also be subject to dropout due to several competing causes, the likelihood of which may be affected by the levels of these biomarkers. In this dissertation, we investigate flexible Bayesian modeling of such data, taking into account any available covariate information as well as possible censoring of the drop-out times. We propose joint models for multiple biomarkers with multiple causes of dropout. Our proposed models allow the trajectories to have multiple joinpoints, the locations of which are estimated from the data. We explore two ways of modeling longitudinal data incorporating the dropout information. Dirichlet process priors are used to make the models robust to misspecication. The Dirichlet process also leads to a natural clustering of subjects with similar trajectories, which can be of importance in efficiently estimating the joinpoints. Efficient Markov chain Monte Carlo algorithms are developed for fitting the proposed models. The performance of all the methods is investigated through simulation studies. One of the proposed models is seen to give rise to improved estimates of individual trajectories. Data from ACTG 398 study is used to illustrate the applicability of that model

Unsupervised Domain Adaptive Detection with Network Stability Analysis

Domain adaptive detection aims to improve the generality of a detector, learned from the labeled source domain, on the unlabeled target domain. In this work, drawing inspiration from the concept of stability from the control theory that a robust system requires to remain consistent both externally and internally regardless of disturbances, we propose a novel framework that achieves unsupervised domain adaptive detection through stability analysis. In specific, we treat discrepancies between images and regions from different domains as disturbances, and introduce a novel simple but effective Network Stability Analysis (NSA) framework that considers various disturbances for domain adaptation. Particularly, we explore three types of perturbations including heavy and light image-level disturbances and instancelevel disturbance. For each type, NSA performs external consistency analysis on the outputs from raw and perturbed images and/or internal consistency analysis on their features, using teacher-student models. By integrating NSA into Faster R-CNN, we immediately achieve state-of-the-art results. In particular, we set a new record of 52.7% mAP on Cityscapes-to-FoggyCityscapes, showing the potential of NSA for domain adaptive detection. It is worth noticing, our NSA is designed for general purpose, and thus applicable to one-stage detection model (e.g., FCOS) besides the adopted one, as shown by experiments. https://github.com/tiankongzhang/NSA

Coupled Modeling and Simulation of Phase Transformation in Zircaloy-4 Fuel Cladding Under Loss-of-Coolant Accident Conditions

Under loss-of-coolant conditions, the temperature on fuel cladding will increase rapidly (up to 1000–1500 K), which will not only cause a dramatic oxidation reaction of Zircaloy-4 and an increase in hydrogen concentration but also cause an allotropic phase transformation of Zircaloy-4 from hexagonal (α-pahse) to cubic (β-phase) crystal structure. As we all know, thermophysical properties have a close relationship with the microstructure of the material. Moreover, because of an important influence of the phase transformation on the creep resistance and the ductility of the fuel rod, studying the crystallographic phase transformation kinetics is pivotal for evaluating properties for fuel rod completeness. We coupled the phase transformation model together with the existing physical models for reactor fuel, gap, cladding, and coolant, based on the finite element analysis and simulation software COMSOL Multiphysics. The critical parameter for this transformation is the evolution of the volume fraction of the favored phase described by a function of time and temperature. Hence, we choose two different volume fractions (0 and 10%) of BeO for UO2-BeO enhanced thermal conductivity nuclear fuel and zircaloy cladding as objects of this study. In order to simulate loss-of-coolant accident conditions, five relevant parameters are studied, including the gap size between fuel and cladding, the temperature at the extremities of the fuel element, the coefficient of heat transfer, the linear power rate, and the coolant temperature, to see their influence on the behavior of phase transformation under non-isothermal conditions. The results show that the addition of 10vol%BeO in the UO2 fuel decreased the phase transformation effect a lot, and no significant phase transformation was observed in Zircaloy-4 cladding with UO2-BeO enhanced thermal conductivity nuclear fuel during existing loss-of-coolant accident conditions

Multiphysics Investigation on Coolant Thermohydraulic Conditions and Fuel Rod Behavior During a Loss-of-Coolant Accident

This article simulates the multiphysics coolant thermohydraulic conditions and fuel performance of a pressurized water reactor (PWR) during a loss-of-coolant accident (LOCA). In the coolant channel of a PWR, the coolant undergoes a series of different boiling regimes along the axial direction. At the inlet of the coolant channel, heat exchange between the cladding wall and coolant is based on single-phase forced convection. As the coolant flow distance increases, the boiling regime gradually converts to nucleate boiling. When a LOCA occurs, on the one hand, the coolant flux and coolant pressure decrease sharply; on the other hand, the heat flux at the cladding wall decreases relatively slowly. They both contribute to a swift increase in coolant temperature. As a consequence, a boiling crisis may occur as critical heat flux (CHF) decreases. In this article, the void fraction along the length of coolant channel in a reactor and mechanical performance of Zr cladding enwrapping UO2 fuel are investigated by establishing a fully coupled multiphysics model based on the CAMPUS code. Physical models of coolant boiling regimes are implemented into the CAMPUS code by adopting different heat transfer models and void fraction models. Physical properties of the coolant are implemented into the CAMPUS code using curve-fitting results. All physical models and parameters related to solid heat transfer are implemented into the CAMPUS code with a 2D axisymmetric geometry. The modeling results help enhance our understanding of void fraction along the length of the coolant channel and mechanical performance of Zr cladding enwrapping UO2 fuel under different coolant pressure and mass flux conditions during a LOCA

AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes

Multi-object tracking (MOT) is a fundamental problem in computer vision with numerous applications, such as intelligent surveillance and automated driving. Despite the significant progress made in MOT, pedestrian attributes, such as gender, hairstyle, body shape, and clothing features, which contain rich and high-level information, have been less explored. To address this gap, we propose a simple, effective, and generic method to predict pedestrian attributes to support general Re-ID embedding. We first introduce AttMOT, a large, highly enriched synthetic dataset for pedestrian tracking, containing over 80k frames and 6 million pedestrian IDs with different time, weather conditions, and scenarios. To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes. Subsequently, we explore different approaches to fuse Re-ID embedding and pedestrian attributes, including attention mechanisms, which we hope will stimulate the development of attribute-assisted MOT. The proposed method AAM demonstrates its effectiveness and generality on several representative pedestrian multi-object tracking benchmarks, including MOT17 and MOT20, through experiments on the AttMOT dataset. When applied to state-of-the-art trackers, AAM achieves consistent improvements in MOTA, HOTA, AssA, IDs, and IDF1 scores. For instance, on MOT17, the proposed method yields a +1.1 MOTA, +1.7 HOTA, and +1.8 IDF1 improvement when used with FairMOT. To encourage further research on attribute-assisted MOT, we will release the AttMOT dataset