Collaborative Target Tracking in Elliptic Coordinates: a Binocular Coordination Approach

This paper concentrates on the collaborative target tracking control of a pair of tracking vehicles with formation constraints. The proposed controller requires only distance measurements between tracking vehicles and the target. Its novelty lies in two aspects: 1) the elliptic coordinates are used to represent an arbitrary tracking formation without singularity, which can be deduced from inter-agent distances, and 2) the regulation of the tracking vehicle system obeys a binocular coordination principle, which simplifies the design of the control law by leveraging rich physical meanings of elliptic coordinates. The tracking system with the proposed controller is proven to be exponentially convergent when the target is stationary. When the target drifts with a small velocity, the desired tracking formation is achieved within a small margin proportional to the magnitude of the target's drift velocity. Simulation examples are provided to demonstrate the tracking performance of the proposed controller.Comment: 6 pages, 5 figure

Kidney function and cardiovascular diseases: a large-scale observational and Mendelian randomization study

BackgroundPrior observational studies have found an association between kidney function and cardiovascular diseases (CVDs). However, these studies did not investigate causality. Therefore, the aim of this study is to examine the causal relationship between kidney function and CVDs.MethodsWe utilized data from the eICU Collaborative Research Database (eICU-CRD) from the years 2014-2015 to evaluate the observational association between renal failure (RF) and CVDs. To investigate the causal effects of kidney function (estimated glomerular filtration rate [eGFR] and chronic kidney disease [CKD]) and CVDs (including atrial fibrillation [AF], coronary artery disease [CAD], heart failure [HF], any stroke [AS], and any ischemic stroke [AIS]), we conducted a two-sample bidirectional Mendelian randomization (MR) analysis.ResultsIn the observational analysis, a total of 157,883 patients were included. After adjusting for potential confounding factors, there was no significant association between baseline RF and an increased risk of developing CVDs during hospitalization [adjusted odds ratio (OR): 1.056, 95% confidence interval (CI): 0.993 to 1.123, P = 0.083]. Conversely, baseline CVDs was significantly associated with an increased risk of developing RF during hospitalization (adjusted OR: 1.189, 95% CI: 1.139 to 1.240, P < 0.001). In the MR analysis, genetically predicted AF was associated with an increased risk of CKD (OR: 1.050, 95% CI: 1.016 to 1.085, P = 0.004). HF was correlated with lower eGFR (β: -0.056, 95% CI: -0.090 to -0.022, P = 0.001). A genetic susceptibility for AS and AIS was linked to lower eGFR (β: -0.057, 95% CI: -0.079 to -0.036, P < 0.001; β: -0.029, 95% CI: -0.050 to -0.009, P = 0.005; respectively) and a higher risk of CKD (OR: 1.332, 95% CI: 1.162 to 1.528, P < 0.001; OR: 1.197, 95% CI: 1.023 to 1.400, P = 0.025; respectively). Regarding the reverse direction analysis, there was insufficient evidence to prove the causal effects of kidney function on CVDs. Outcomes remained consistent in sensitivity analyses.ConclusionOur study provides evidence for causal effects of CVDs on kidney function. However, the evidence to support the causal effects of kidney function on CVDs is currently insufficient. Further mechanistic studies are required to determine the causality

The next widespread bamboo flowering poses a massive risk to the giant panda

The IUCN Red List has downgraded several species from “endangered” to “vulnerable” that still have largely unknown extinction risks. We consider one of those downgraded species, the giant panda, a bamboo specialist. Massive bamboo flowering could be a natural disaster for giant pandas. Using scenario analysis, we explored possible impacts of the next bamboo flowering in the Qinling and Minshan Mountains that are home to most giant pandas. Our results showed that the Qinling Mountains could experience large-scale bamboo flowering leading to a high risk of widespread food shortages for the giant pandas by 2020. The Minshan Mountains could similarly experience a large-scale bamboo flowering with a high risk for giant pandas between 2020 and 2030 without suitable alternative habitat in the surrounding areas. These scenarios highlight thus-far unforeseen dangers of conserving giant pandas in a fragmented habitat. We recommend advance measures to protect giant panda from severe population crashes when flowering happens. This study also suggests the need to anticipate and manage long-term risks to other downgraded species

Accurate Object Recognition with Assembling Appearance and Motion Information

How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding

Accurate Object Recognition with Assembling Appearance and Motion Information

How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding