Models and algorithms for multi-agent search problems

The problem of searching for objects of interest occurs in important applications ranging from rescue, security, transportation, to medicine. With the increasing use of autonomous vehicles as search platforms, there is a need for fast algorithms that can generate search plans for multiple agents in response to new information. In this dissertation, we develop new techniques for automated generation of search plans for different classes of search problems. First, we study the problem of searching for a stationary object in a discrete search space with multiple agents where each agent can access only a subset of the search space. In these problems, agents can fail to detect an object when inspecting a location. We show that when the probabilities of detection only depend on the locations, this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm finds the optimal solution in finite time, and has worst-case computation performance that is faster than general minimum cost flow algorithms. We then generalize it to the case where the probabilities of detection depend on the agents and the locations, and propose a greedy algorithm that is 1/2-approximate. Second, we study the problem of searching for a moving object in a discrete search space with multiple agents where each agent can access only a subset of a discrete search space at any time and agents can fail to detect objects when searching a location at a given time. We provide necessary conditions for an optimal search plan, extending prior results in search theory. For the case where the probabilities of detection depend on the locations and the time periods, we develop a forward-backward iterative algorithm based on coordinate descent techniques to obtain solutions. To avoid local optimum, we derive a convex relaxation of the dynamic search problem and show this can be solved optimally using coordinate descent techniques. The solutions of the relaxed problem are used to provide random starting conditions for the iterative algorithm. We also address the problem where the probabilities of detection depend on the agents as well as the locations and the time periods, and show that a greedy-style algorithm is 1/2-approximate. Third, we study problems when multiple objects of interest being searched are physically scattered among locations on a graph and the agents are subject to motion constraints captured by the graph edges as well as budget constraints. We model such problem as an orienteering problem, when searching with a single agent, or a team orienteering problem, when searching with multiple agents. We develop novel real-time efficient algorithms for both problems. Fourth, we investigate classes of continuous-region multi-agent adaptive search problems as stochastic control problems with imperfect information. We allow the agent measurement errors to be either correlated or independent across agents. The structure of these problems, with objectives related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost. We derive a lower bound on the performance of the minimum mean-square error estimator, and provide upper bounds on the estimation error for special cases. For agents with independent errors, we show that the optimal sensing strategies can be obtained in terms of the solution of decoupled scalar convex optimization problems, followed by a joint region selection procedure. We further consider search of multiple objects and provide an explicit construction for adaptively determining the sensing actions

Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics

Quantitative assessment of renal functions using 68Ga-EDTA dynamic PET imaging in renal injury in mice of different origins

BackgroundEarly detection of kidney diseases can be challenging as conventional methods such as blood tests or imaging techniques (computed tomography (CT), magnetic resonance imaging (MRI), or ultrasonography) may be insufficient to assess renal function. A single-photon emission CT (SPECT) renal scan provides a means of measuring glomerular filtration rates (GFRs), but its diagnostic accuracy is limited due to its planar imaging modality and semi-quantification property. In this study, we aimed to improve the accuracy of GFR measurement by preparing a positron emission tonometry (PET) tracer 68Ga-Ethylenediaminetetraacetic acid (68Ga-EDTA) and comprehensively evaluating its performance in healthy mice and murine models of renal dysfunction.MethodsDynamic PET scans were performed in healthy C57BL/6 mice and in models of renal injury, including acute kidney injury (AKI) and unilateral ureter obstruction (UUO) using 68Ga-EDTA. In a 30-min dynamic scan, PET images and time-activity curves (TACs) were acquired. Renal function and GFR values were measured using renograms and validated through serum renal function parameters, biodistribution results, and pathological staining.Results68Ga-EDTA dynamic PET imaging quantitatively captured the tracer elimination process. The calculated GFR values were 0.25 ± 0.02 ml/min in healthy mice, 0.01 ± 0.00 ml/min in AKI mice, and 0.25 ± 0.04, 0.29 ± 0.03 and 0.24 ± 0.01 ml/min in UUO mice, respectively. Furthermore, 68Ga-EDTA dynamic PET imaging and GFRPET were able to differentiate mild renal impairment before serum parameters indicated any changes.ConclusionsOur findings demonstrate that 68Ga-EDTA dynamic PET provides a reliable and precise means of evaluating renal function in two murine models of renal injury. These results hold promise for the widespread clinical application of 68Ga-EDTA dynamic PET in the near future

Predictive value of intratumoral-metabolic heterogeneity derived from 18F-FDG PET/CT in distinguishing microsatellite instability status of colorectal carcinoma

Purpose/backgroundMicrosatellite instability (MSI) status is a significant biomarker for the response to immune checkpoint inhibitors, response to 5-fluorouracil-based adjuvant chemotherapy, and prognosis in colorectal carcinoma (CRC). This study investigated the predictive value of intratumoral-metabolic heterogeneity (IMH) and conventional metabolic parameters derived from 18F-FDG PET/CT for MSI in patients with stage I–III CRC.MethodsThis study was a retrospective analysis of 152 CRC patients with pathologically proven MSI who underwent 18F-FDG PET/CT examination from January 2016 to May 2022. Intratumoral-metabolic heterogeneity (including heterogeneity index [HI] and heterogeneity factor [HF]) and conventional metabolic parameters (standardized uptake value [SUV], metabolic tumor volume [MTV], and total lesion glycolysis [TLG]) of the primary lesions were determined. MTV and SUVmean were calculated on the basis of the percentage threshold of SUVs at 30%–70%. TLG, HI, and HF were obtained on the basis of the above corresponding thresholds. MSI was determined by immunohistochemical evaluation. Differences in clinicopathologic and various metabolic parameters between MSI-High (MSI-H) and microsatellite stability (MSS) groups were assessed. Potential risk factors for MSI were assessed by logistic regression analyses and used for construction of the mathematical model. Area under the curve (AUC) were used to evaluate the predictive ability of factors for MSI.ResultsThis study included 88 patients with CRC in stages I–III, including 19 (21.6%) patients with MSI-H and 69 (78.4%) patients with MSS. Poor differentiation, mucinous component, and various metabolic parameters including MTV30%, MTV40%, MTV50%, and MTV60%, as well as HI50%, HI60%, HI70%, and HF in the MSI-H group were significantly higher than those in the MSS group (all P < 0.05). In multivariate logistic regression analyses, post-standardized HI60% by Z-score (P = 0.037, OR: 2.107) and mucinous component (P < 0.001, OR:11.394) were independently correlated with MSI. AUC of HI60% and our model of the HI60% + mucinous component was 0.685 and 0.850, respectively (P = 0.019), and the AUC of HI30% in predicting the mucinous component was 0.663.ConclusionsIntratumoral-metabolic heterogeneity derived from 18F-FDG PET/CT was higher in MSI-H CRC and predicted MSI in stage I–III CRC patients preoperatively. HI60% and mucinous component were independent risk factors for MSI. These findings provide new methods to predict the MSI and mucinous component for patients with CRC