A Source-Initiated On-Demand Routing Algorithm Based on the Thorup-Zwick Theory for Mobile Wireless Sensor Networks

The unreliability and dynamics of mobile wireless sensor networks make it hard to perform end-to-end communications. This paper presents a novel source-initiated on-demand routing mechanism for efficient data transmission in mobile wireless sensor networks. It explores the Thorup-Zwick theory to achieve source-initiated on-demand routing with time efficiency. It is able to find out shortest routing path between source and target in a network and transfer data in linear time. The algorithm is easy to be implemented and performed in resource-constrained mobile wireless sensor networks. We also evaluate the approach by analyzing its cost in detail. It can be seen that the approach is efficient to support data transmission in mobile wireless sensor networks

Characterizing Alzheimer's Disease Biomarker Cascade Through Non-linear Mixed Effect Models

Alzheimer's Disease (AD) research has shifted to focus on biomarker trajectories and their potential use in understanding the underlying AD-related pathological process. A conceptual framework was proposed in such modern AD research that hypothesized biomarker cascades as a result of underlying AD pathology. In this paper, we leverage the idea of biomarker cascades and develop methods that use a non-linear mixed effect model to depict AD biomarker trajectories as a function of the latent AD disease progression. We tailored our methods to address a number of real-data challenges present in BIOCARD and ADNI studies. We illustrate the proposed methods with simulation studies as well as analysis results on the BIOCARD and ADNI data showing the ordering of various biomarkers from the CSF, MRI, and cognitive domains. We investigated cascading patterns of AD biomarkers in these datasets and presented prediction results for individual-level profiles over time. These findings highlight the potential of the conceptual biomarker cascade framework to be leveraged for diagnoses and monitoring.Comment: 28 pages, 2 figures, 3 table

Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints

Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization. One popular approach to handling these complex scenarios is Bayesian Optimization (BO). In terms of theoretical behavior, BO is relatively well understood in the unconstrained setting, where its principles have been well explored and validated. However, when it comes to constrained Bayesian optimization (CBO), the existing framework often relies on heuristics or approximations without the same level of theoretical guarantees. In this paper, we delve into the theoretical and practical aspects of constrained Bayesian optimization, where the objective and constraints can be independently evaluated and are subject to noise. By recognizing that both the objective and constraints can help identify high-confidence regions of interest (ROI), we propose an efficient CBO framework that intersects the ROIs identified from each aspect to determine the general ROI. The ROI, coupled with a novel acquisition function that adaptively balances the optimization of the objective and the identification of feasible regions, enables us to derive rigorous theoretical justifications for its performance. We showcase the efficiency and robustness of our proposed CBO framework through empirical evidence and discuss the fundamental challenge of deriving practical regret bounds for CBO algorithms

Optimal Decision Rule for Combining Multiple Biomarkers into Tree-based Classifier and its Evaluation

In biomedical practices, multiple biomarkers are often combined using a classification rule of the form of some tree structure to make diagnostic decisions. The classification structure and cutoff point at each node of a tree are commonly chosen ad-hoc based on experience of decision makers. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points of a pre-specified classification tree. In this dissertation, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible and computationally feasible using data with reasonably large sample sizes when there are many biomarkers available for classification or prediction. Using this method, for a pre-specified tree-structured classification rule, we are able to guide the choice of optimal cutoff at tree nodes, as well as to estimate optimal prediction performance of multiple biomarkers combined. In this dissertation, we also propose a semi-marginal and semi-parametric regression model for gap times between successive recurrent events in the presence of time-dependent covariates. Recurrent event data is commonly encountered in longitudinal follow-up studies, when each subject experiences multiple events under observation until loss to follow-up, dropout or end of study occurs. There exists a rich literature of models and methods that focus on time-to-event data in a recurrent event setting, but for applications where time- between-events (also referred to as gap times) is of scientific interest or where there is a strong cyclical pattern, limited techniques were developed, especially for regression with time-dependent covariates. We propose a semi-marginal regression model of a proportional hazard form on gap times such that no event history is included in the conditional statistics of regression except for the time relapse from baseline to last event occurrence. The proposed method is flexible in being semi-parametric, robust to various correlation structures of gap times within subject, and also allows time-dependent covariates to be included in the conditional statistics of regression

Uncovering the human motion pattern: Pattern Memory-based Diffusion Model for Trajectory Prediction

Human trajectory forecasting is a critical challenge in fields such as robotics and autonomous driving. Due to the inherent uncertainty of human actions and intentions in real-world scenarios, various unexpected occurrences may arise. To uncover latent motion patterns in human behavior, we introduce a novel memory-based method, named Motion Pattern Priors Memory Network. Our method involves constructing a memory bank derived from clustered prior knowledge of motion patterns observed in the training set trajectories. We introduce an addressing mechanism to retrieve the matched pattern and the potential target distributions for each prediction from the memory bank, which enables the identification and retrieval of natural motion patterns exhibited by agents, subsequently using the target priors memory token to guide the diffusion model to generate predictions. Extensive experiments validate the effectiveness of our approach, achieving state-of-the-art trajectory prediction accuracy. The code will be made publicly available