DETC2005-85726 IMPROVED CURRENT STATISTIC MODEL AND ADAPTIVE FILTERING

ABSTRACT Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets. So it may be difficult to meet all maneuvering conditions. In this paper a novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. The Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current statistical model. Keywords: Current statistical model; Fuzzy logic; Unscented transformation INTRODUCTION The problem of tracking maneuvering targets has received a great of attention. The key to this problem lies in building the optimal target motion model. Various mathematical models of target motion have been developed over the past three decades, among which interacting multiple model (IMM) and current statistical model (CSM) are representativ

Follicular dendritic cell sarcoma: a report of six cases and a review of the Chinese literature

<p>Abstract</p> <p>Goals</p> <p>The main purpose of this study is to broaden the clinicopathological spectrum and increase recognition of follicular dendritic cell sarcoma (FDCS) through analysis of the clinical and pathological features of 50 cases.</p> <p>Methods</p> <p>The clinicopathological features of total 50 cases of FDCS were analyzed including a review of 44 cases reported in Chinese literature before October 2009 and six original cases from the pathology files conducted by the authors.</p> <p>Results</p> <p>The youngest patient came under observation in this study is only seven years old. Including the cases contributed by the authors, our literary review indicated that male dominated the tumor cases (M: F = 3: 2). 28 cases (56%) present with this disease in extranodal sites. Tumor cells demonstrated positive staining for the follicular dendritic cell markers CD21 (47/49), CD35 (43/45), CD23 (20/23) and CD68 (23/25). In situ hybridization for Epstein-Barr virus-encoded RNA was performed in 10 cases. Nevertheless, EBV expression was absent in all these cases. The follow-up analysis of all cases shows that 26 (81.2%) patients were alive and disease free; 6 (18.8%) patients were alive with recurrent disease or metastasis; and nobody had died of this disease at the time of last follow-up.</p> <p>Conclusions</p> <p>The diagnosis of the FDCS is based on the findings of morphology and immunohistochemistry. The FDCS occurred in China should be viewed and treated as a low-grade sarcoma, and the role of the EBV in the pathogenesis of this tumor is still uncertain. There is a possibility that the tumor might be racial or geographic correlated, because most cases were reported from Eastern Asia area; it's particular the case of the liver or spleen tumor.</p

Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models

Recent advancements in foundation models (FMs), such as GPT-4 and LLaMA, have attracted significant attention due to their exceptional performance in zero-shot learning scenarios. Similarly, in the field of visual learning, models like Grounding DINO and the Segment Anything Model (SAM) have exhibited remarkable progress in open-set detection and instance segmentation tasks. It is undeniable that these FMs will profoundly impact a wide range of real-world visual learning tasks, ushering in a new paradigm shift for developing such models. In this study, we concentrate on the remote sensing domain, where the images are notably dissimilar from those in conventional scenarios. We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg. The pipeline is benchmarked on several widely-used remote sensing datasets, and we present preliminary results to demonstrate its effectiveness. Through this work, we aim to provide insights into maximizing the applicability of visual FMs in specific contexts with minimal model tuning. The code is available at https://github.com/Douglas2Code/Text2Seg.Comment: 10 pages, 6 figure

Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations

Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to high inter-operator variability, resulting images highly depend on operators' experience. In this work, an intelligent robotic sonographer is proposed to autonomously "explore" target anatomies and navigate a US probe to a relevant 2D plane by learning from expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparisons approach in a self-supervised fashion. This process can be referred to as understanding the "language of sonography". Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly extract the task-related and domain features in latent space. Besides, a Gaussian distribution-based filter is developed to automatically evaluate and take the quality of the expert's demonstrations into account. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated to B-mode images. To demonstrate the performance of the proposed approach, representative experiments for the "line" target and "point" target are performed on vascular phantom and two ex-vivo animal organ phantoms (chicken heart and lamb kidney), respectively. The results demonstrated that the proposed advanced framework can robustly work on different kinds of known and unseen phantoms

Explaining Income-Related Inequalities in Dietary Knowledge: Evidence from the China Health and Nutrition Survey

Lack of adequate dietary knowledge may result in poor health conditions. This study aims to measure income-related inequality in dietary knowledge, and to explain the sources of the inequality. Data were from the China Health and Nutrition Survey (CHNS) conducted in 2015. A summary of the dietary knowledge score and dietary guideline awareness was used to measure the dietary knowledge of respondents. The concentration index was employed as a measure of socioeconomic inequality and was decomposed into its determining factors. The study found that the proportion of respondents who correctly answered questions on dietary knowledge was significantly low for some questions. Compared to rural residents, urban residents had a higher proportion of correctly answered dietary knowledge questions. In addition, there are pro-rich inequalities in dietary knowledge. This observed inequality is determined not only by individual factors but also high-level area factors. Our study recommends that future dietary education programs could take different strategies for individuals with different educational levels and focus more on disadvantaged people. It would be beneficial to consider local dietary habits in developing education materials