Near-optimal probabilistic search using spatial Fourier sparse set

2017 ◽  
Vol 42 (2) ◽  
pp. 329-351 ◽  
Author(s):  
Kuo-Shih Tseng ◽  
Bérénice Mettler
Keyword(s):  
Probabilistic Search ◽  
Sparse Set

Falsification of Hybrid Systems Using Adaptive Probabilistic Search

2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo
Keyword(s):  
Hybrid Systems ◽  
Input Signal ◽  
Tree Search ◽  
Single Trial ◽  
Decision Point ◽  
Directed Tree ◽  
Probabilistic Choice ◽  
Original Algorithm ◽  
Fine Grained ◽  
Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

Abstract 041: Cardiovascular Disease, Hypertension, Diabetes And Cystatin C Jointly Predict Covid-19 Mortality Alongside Age, Male Sex And Black Ethnicity

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Joshua Elliott ◽  
Barbara Bodinier ◽  
Matthew Whitaker ◽  
Ioanna Tzoulaki ◽  
Paul Elliott ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Stability Analysis ◽  
Cystatin C ◽  
Penalized Regression ◽  
Pressure Control ◽  
Stability Selection ◽  
Selection Threshold ◽  
Male Sex ◽  
Sparse Set

Introduction: Studies of risk factors for severe/fatal COVID-19 to date may not have identified the optimal set of informative predictors. Hypothesis: Use of penalized regression with stability analysis may identify new, sparse sets of risk factors jointly associated with COVID-19 mortality. Methods: We investigated demographic, social, lifestyle, biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and air pollution data from UK Biobank (N=473,574) in relation to linked COVID-19 mortality, and compared with non-COVID-19 mortality. We used penalized regression models (LASSO) with stability analysis (80% selection threshold from 1,000 models with 80% subsampling) to identify a sparse set of variables associated with COVID-19 mortality. Results: Among 43 variables considered by LASSO stability selection, cardiovascular disease, hypertension, diabetes, cystatin C, age, male sex and Black ethnicity were jointly predictive of COVID-19 mortality risk at 80% selection threshold (Figure). Of these, Black ethnicity and hypertension contributed to COVID-19 but not non-COVID-19 mortality. Conclusions: Use of LASSO stability selection identified a sparse set of predictors for COVID-19 mortality including cardiovascular disease, hypertension, diabetes and cystatin C, a marker of renal function that has also been implicated in atherogenesis and inflammation. These results indicate the importance of cardiometabolic comorbidities as predisposing factors for COVID-19 mortality. Hypertension was differentially highly selected for risk of COVID-19 mortality, suggesting the need for continued vigilance with good blood pressure control during the pandemic.

Model Predictive Control-Based Probabilistic Search Method for Autonomous Ground Robot in a Dynamic Environment

2015 ◽  
Author(s):  
Chang Liu ◽  
Shengbo Eben Li ◽  
J. Karl Hedrick
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Gaussian Function ◽  
Dynamic Environment ◽  
Gaussian Mixture ◽  
Search Method ◽  
Stationary Target ◽  
Probability Map ◽  
Probabilistic Search ◽  
Binary Sensor

Target search using autonomous robots is an important application for both civil and military scenarios. In this paper, a model predictive control (MPC)-based probabilistic search method is presented for a ground robot to localize a stationary target in a dynamic environment. The robot is equipped with a binary sensor for target detection, of which the uncertainties of binary observation are modeled as a Gaussian function. Under the model predictive control framework, the probability map of the target is updated via the recursive Bayesian estimation and the collision avoidance with obstacles is enforced using barrier functions. By approximating the updated probability map using a Gaussian Mixture Model, an analytical form of the objective function in the prediction horizon is derived, which is promising to reduce the computation complexity compared to numerical integration methods. The effectiveness of the proposed method is demonstrated by performing simulations in dynamic scenarios with both static and moving obstacles.

3D curve structure reconstruction from a sparse set of unordered images

2009 ◽  
Vol 60 (2) ◽  
pp. 126-134 ◽  
Author(s):  
Zheng Jian-dong ◽  
Zhang Li-yan ◽  
Du Xiao-yu ◽  
Ding Zhi-an
Keyword(s):  
Curve Structure ◽  
Structure Reconstruction ◽  
Sparse Set

scholarly journals Algorithm for Counting Cars in Large-scale Video Surveillance Systems

2020 ◽  
pp. 100-108
Author(s):  
Arsenii Shirokov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Video Surveillance ◽  
Large Scale ◽  
Surveillance Systems ◽  
Motion Model ◽  
People Tracking ◽  
Detection Frequency ◽  
Computational Resources ◽  
Sparse Set ◽  
The Given ◽  
Distributed Tracking

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

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