arXiv subjects

Weiping Zhu

Publications and source records attributed to Weiping Zhu.

13 recordsLinked to original sources

arXiv2025

ReNiL: Event-Driven Pedestrian Bayesian Localization Using IMU for Real-World Applications

Pedestrian inertial localization is key for mobile and IoT services because it provides infrastructure-free positioning. Yet most learning-based methods depend on fixed sliding-window integration, struggle to adapt to diverse motion scales and cadences, and yield inconsistent uncertainty, limiting real-world use. We present ReNiL, a Bayesian deep-learning framework for accurate, efficient, and uncertainty-aware pedestrian localization. ReNiL introduces Inertial Positioning Demand Points (IPDPs) to estimate motion at contextually meaningful waypoints instead of dense tracking, and supports inference on IMU sequences at any scale so cadence can match application needs. It couples a motion-aware orientation filter with an Any-Scale Laplace Estimator (ASLE), a dual-task network that blends patch-based self-supervision with Bayesian regression. By modeling displacements with a Laplace distribution, ReNiL provides homogeneous Euclidean uncertainty that integrates cleanly with other sensors. A Bayesian inference chain links successive IPDPs into consistent trajectories. On RoNIN-ds and a new WUDataset covering indoor and outdoor motion from 28 participants, ReNiL achieves state-of-the-art displacement accuracy and uncertainty consistency, outperforming TLIO, CTIN, iMoT, and RoNIN variants while reducing computation. Application studies further show robustness and practicality for mobile and IoT localization, making ReNiL a scalable, uncertainty-aware foundation for next-generation positioning.

cs.RO↗

arXiv2024

Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality's importance and cross-modal cues for the efficient adaptation of parameters in visual-language models. This work introduces a cross-modal parameter-efficient approach named XMAdapter. XMAdapter establishes cache models for both text and image modalities. It then leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, it achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Extensive experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency.

cs.CV↗

arXiv2017

Statistical Properties of Loss Rate Estimators in Tree Topology (2)

Four types of explicit estimators are proposed here to estimate the loss rates of the links in a network with the tree topology and all of them are derived by the maximum likelihood principle. One of the four is developed from an estimator that was used but neglected because it was suspected to have a higher variance. All of the estimators are proved to be either unbiased or asymptotic unbiased. In addition, a set of formulae are derived to compute the efficiencies and variances of the estimates obtained by the estimators. One of the formulae shows that if a path is divided into two segments, the variance of the estimates obtained for the pass rate of a segment is equal to the variance of the pass rate of the path divided by the square of the pass rate of the other segment. A number of theorems and corollaries are derived from the formulae that can be used to evaluate the performance of an estimator. Using the theorems and corollaries, we find the estimators from the neglected one are the best estimator for the networks with the tree topology in terms of efficiency and computation complexity.

cs.NI↗

arXiv2016

Statistical Properties of Loss Rate Estimators in Tree Topology

Three types of explicit estimators are proposed here to estimate the loss rates of the links in a network of the tree topology. All of them are derived by the maximum likelihood principle and proved to be either asymptotic unbiased or unbiased. In addition, a set of formulae are derived to compute the efficiencies and variances of the estimators that also cover some of the estimators proposed previously. The formulae unveil that the variance of the estimates obtained by a maximum likelihood estimator for the pass rate of the root link of a multicast tree is equal to the variance of the pass rate of the multicast tree divided by the pass rate of the tree connected to the root link. Using the formulae, we are able to evaluate the estimators proposed so far and select an estimator for a data set.

cs.NI↗

arXiv2016

Loss Tomography in General Topology

Although there are a few works reported in the literature considering loss tomography in the general topology, there is few well established result since all of them rely either on simulations or on experiments that have many random factors affecting the outcome. To improve the situation, we address a number of issues in this paper that include a maximum likelihood estimator (MLE) for the general topology, the statistical properties of the MLE, the statistical properties of a frequently referred estimator called the moving variance and weighted average (MVWA), and a renewed MVWA that removes the restriction of knowing variance in advance from the MVWA. The statistical properties covers minimum-variance unbiasedness, efficiency, and variances of the estimates obtained by the estimators. Given the properties, we can evaluate the estimators without the need of simulations. To verify the properties, a simulation study is conducted that confirms the accuracy of the findings.

cs.NI↗

arXiv2015

Fast Parameter Estimation in Loss Tomography for Networks of General Topology

As a technique to investigate link-level loss rates of a computer network with low operational cost, loss tomography has received considerable attentions in recent years. A number of parameter estimation methods have been proposed for loss tomography of networks with a tree structure as well as a general topological structure. However, these methods suffer from either high computational cost or insufficient use of information in the data. In this paper, we provide both theoretical results and practical algorithms for parameter estimation in loss tomography. By introducing a group of novel statistics and alternative parameter systems, we find that the likelihood function of the observed data from loss tomography keeps exactly the same mathematical formulation for tree and general topologies, revealing that networks with different topologies share the same mathematical nature for loss tomography. More importantly, we discover that a re-parametrization of the likelihood function belongs to the standard exponential family, which is convex and has a unique mode under regularity conditions. Based on these theoretical results, novel algorithms to find the MLE are developed. Compared to existing methods in the literature, the proposed methods enjoy great computational advantages.

stat.ME↗

arXiv2015

Loss Rate Estimators and the Properties for the Tree Topology

A large number of explicit estimators are proposed in this paper for loss rate estimation in a network of the tree topology. All of the estimators are proved to be unbiased and consistent instead of asymptotic unbiased as that obtained in [1] for a specific estimator. In addition, a set of formulae are derived for the variances of various maximum likelihood estimators that unveil the connection between the path of interest and the subtrees connecting the path to observers. Using the formulae, we are able to not only rank the estimators proposed so far, including those proposed in this paper, but also identify the errors made in previous works. More importantly, using the formulae we can easily identify the most efficient explicit estimator from a pool that makes model selection feasible in loss tomography

cs.IT↗

arXiv2013

Explicit Estimators for Loss Tomography

Full likelihood has been widely used in loss tomography because most believe it can produce accurate estimates although the full likelihood estimators proposed so far are complex in structure and expensive in execution. We in this paper advocate a different likelihood called composite likelihood to replace the full likelihood in loss tomography for simplicity and accuracy. Using the proposed likelihood, we propose a number of explicit estimators with statistical analysis. The analysis shows all of the explicit estimators perform almost as good as the full likelihood one in terms of accuracy and better than the full likelihood one in computational complexity. Although the discussion is restricted to the tree topology, the methodology proposed here is also applicable to a network of a general topology.

stat.ME↗

arXiv2012

Loss Tomography from Tree Topologies to General Topologies

Loss tomography has received considerable attention in recent years and a number of estimators based on maximum likelihood (ML) or Bayesian principles have been proposed. Almost all of the estimators are devoted to the tree topology despite the general topology is more common in practice. There has been few likelihood function devoted to the general topology, not to mention the estimator. To overcome this, two sets of sufficient statistics for the tree and general topologies, respectively, are proposed in this paper. Using the statistics, two likelihood functions, one for a topology, are proposed here and subsequently two likelihood equations for the general topology, one is link-based and the other is path-based, are obtained. In addition, a dependence between subtrees in terms of their estimates is identified for the general topology and a divide-and-conquer strategy is proposed to deal with the dependence, which divides a general network into two types of independent trees. Further, two algorithms, one for a type of the independent trees, are proposed to estimate the loss rates of each type.

cs.NI↗

arXiv2012

A Closed Form Maximum Likelihood Estimator to End-to-End Loss Rate Estimation

Loss tomography has been studied for more than 10 years and a number of estimators have been proposed. The estimators can be divided into two classes: maximum likelihood and non-maximum likelihood. The maximum likelihood estimators rely on the maximum likelihood principle to ensure the accuracy of the estimates obtained by the estimators. Unfortunately, all of the maximum likelihood estimators need to use an iterative procedure to search the solution space for the maximum or to solve a high degree polynomial. An iterative procedure can be computationally expensive and may even converge to a local maximum. On the other hand, the non-maximum likelihood estimators pursue closed form solutions by scarifying the accuracy of estimates. To overcome the pitfalls, we, in this paper, propose a closed form and maximum likelihood estimator to estimate the loss rate of a link in a network. The closed form solution is built on the discovery of a connection between the number of probes passing a link and the number of probes passing its parent. The proposed estimator is applicable to both the tree topology and the general one.

cs.NI↗

arXiv2011

Loss Rate Inference in Multi-Sources and Multicast-Based General Topology

Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Unfortunately, almost all of them are devoted to the tree topology despite the general topology is more common in practice. In addition, most of the works presented in the literature rely on iterative approximation to search for the maximum of a likelihood function formed from observations, which have been known neither scalable nor efficient. In contrast to the tree topology, there is few paper dedicated to the general topology because of the lack of understanding the impacts created by the probes sent by different sources. We in this paper present the analytical results obtained recently for the general topology that show the correlation created by the probes sent by multiple sources to a node located in an intersection of multiple trees. The correlation is expressed by a set of polynomials of the pass rates of the paths connecting the sources to the node. In addition to the expression, a closed form solution is proposed to obtain the MLE of the pass rates of the paths connecting the sources to the node. Then, two strategies are proposed to estimate the loss rate of a link for the general topology: one is path-based and the other is link-based, depending on whether we need to obtain the pass rate of a path first. The two strategies are compared in the context of the general topology that shows each has its advantages and the link-based one is more general. Apart from proving the estimates obtained are the MLEs, we prove the estimator presented here has the optimal asymptotic property.

cs.NI↗

arXiv2011

Fitting a Model to Data in Loss Tomography

Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Although most of the estimators claim to be the maximum likelihood estimators, the claim is only partially true since the maximum likelihood estimate can be obtained at most for a class of data sets. Unfortunately, few people are aware of this restriction that leads to a misconception that an estimator is applicable to all data sets as far as it returns a unique solution. To correct this, we in this paper point out the risk of this misconception and illustrate the inconsistency between data and model in the most influential estimators. To ensure the model used in estimation consistent with the data collected from an experiment, the data sets used in estimation are divided into 4 classes according to the characteristics of observations. Based on the classification, the validity of an estimator is defined and the validity of the most influential estimators is evaluated. In addition, a number of estimators are proposed, one for a class of data sets that have been overlooked. Further, a general estimator is proposed that is applicable to all data classes. The discussion starts from the tree topology and end at the general topology.

cs.NI↗

arXiv2010

Explicit Maximum Likelihood Loss Estimator in Multicast Tomography

For the tree topology, previous studies show the maximum likelihood estimate (MLE) of a link/path takes a polynomial form with a degree that is one less than the number of descendants connected to the link/path. Since then, the main concern is focused on searching for methods to solve the high degree polynomial without using iterative approximation. An explicit estimator based on the Law of Large Numbers has been proposed to speed up the estimation. However, the estimate obtained from the estimator is not a MLE. When $n<\infty$, the estimate may be noticeable different from the MLE. To overcome this, an explicit MLE estimator is presented in this paper and a comparison between the MLE estimator and the explicit estimator proposed previously is presented to unveil the insight of the MLE estimator and point out the pitfall of the previous one.

cs.NI↗