High Probability Regions Research Articles

Battery remaining useful life prediction using improved mutated particle filter

AbstractAccurate prediction of lithium‐ion battery remaining useful life (RUL) is of great significance for battery health management. Particle filter (PF) is used to predict the RUL effectively, but it suffers from particle degeneracy and impoverishment during the estimation. To solve this problem, this paper proposes a new RUL prediction model based on improved mutated particle filter (IMPF) approach. In the IMPF algorithm, the mutant particles are generated from the prior particles, which not only represent the high probability region but also accelerate the convergence speed of particles. Then an improved polynomial resampling algorithm is proposed to avoid the loss of particle diversity during the resampling process. The simulation results on benchmark function demonstrate the effectiveness of the proposed IMPF algorithm. The method is also applied successfully to a battery capacity degradation data set provided by NASA Ames Research Center. Research results show that the proposed RUL prediction approach has better performance than other related PF techniques.

A search for optical and near-infrared counterparts of the compact binary merger GW190814

We report on our observing campaign of the compact binary merger GW190814, detected by the Advanced LIGO and Advanced Virgo detectors on August 14th, 2019. This signal has the best localisation of any observed gravitational wave (GW) source, with a 90% probability area of 18.5 deg$^2$, and an estimated distance of ~ 240 Mpc. We obtained wide-field observations with the Deca-Degree Optical Transient Imager (DDOTI) covering 88% of the probability area down to a limiting magnitude of $w$ = 19.9 AB. Nearby galaxies within the high probability region were targeted with the Lowell Discovery Telescope (LDT), whereas promising candidate counterparts were characterized through multi-colour photometry with the Reionization and Transients InfraRed (RATIR) and spectroscopy with the Gran Telescopio de Canarias (GTC). We use our optical and near-infrared limits in conjunction with the upper limits obtained by the community to constrain the possible electromagnetic counterparts associated with the merger. A gamma-ray burst seen along its jet's axis is disfavoured by the multi-wavelength dataset, whereas the presence of a burst seen at larger viewing angles is not well constrained. Although our observations are not sensitive to a kilonova similar to AT2017gfo, we can rule out high-mass (> 0.1 M$_{\odot}$) fast-moving (mean velocity >= 0.3c) wind ejecta for a possible kilonova associated with this merger.

Fast ship detection combining visual saliency and a cascade CNN in SAR images

In order to realise the fast detection of ships in synthetic aperture radar (SAR) images, a detection method combining visual saliency and a cascade convolutional neural network (CNN) is proposed. First, based on visual saliency, a multiscale spectral residual model is designed for realising the fast detection of ship candidate regions. Then, a cascaded CNN is designed, which consists of two convolution networks, namely, the front-end shallow CNN, which is used to quickly exclude obvious non-ship candidates and classify the ship candidates according to the ship orientation, and the back-end deep CNN, which is used to detect high-probability candidate regions with rotatable boundary boxes. The whole structure can realise the fast detection and precise positioning of ships with an arbitrary orientation. Finally, the authors conduct detailed experiments on the SAR ship image dataset. The experimental results show that the proposed method can effectively improve the detection accuracy of ships, ensuring the detection efficiency in SAR images.

Vortex streets and honeycomb structures in photodetachment driven by linearly polarized few-cycle laser pulses

Generation of electron vortices in photodetachment driven by linearly polarized laser pulses is demonstrated, both within the strong-field approximation and by numerically solving the time-dependent Schr\odinger equation. The sensitivity of the resulting vortex patterns in the momentum distributions of photoelectrons to the laser pulse parameters (including the pulse duration, its intensity, and the carrier-envelope phase) is analyzed. It is shown that, for a nonzero carrier-envelope phase of the driving pulse, vortex structures in the probability amplitude of detachment occur, similar to von K\'arm\'an vortex streets in fluid mechanics. In addition, hexagonal regions of high probability (honeycomb patterns) are observed, with vortex-antivortex pairs located at their vertices.

An Intelligent Particle Filter With Adaptive M-H Resampling for Liquid-Level Estimation During Silicon Crystal Growth

During the growth of silicon single crystals, it is critical to detect the liquid level of the silicon melt to ensure their high-quality production. Because noise statistics are difficult to determine in measured values of the liquid level, a particle filter (PF) with unknown statistics has been presented to estimate the liquid level. However, this approach leads to inaccurate results due to sample impoverishment. To alleviate this problem, we propose an intelligent PF method with an adaptive Metropolis–Hastings (M-H) resampling strategy. To accomplish this, we first design an M-H resampling strategy with two proposed distributions to resample low-weight particles. These distributions randomly select high-weight particles for the Gaussian mutations or high-weight and low-weight particles for crossover operations, so as to promote the movement of low-weight particles to high-probability regions. We also construct a self-adaptive function to further improve the overall particle quality, which is used to calculate the selection probability of these two proposed distributions according to the proportion of low-weight particles in all of the particles. Finally, the liquid level is estimated according to the particles after the modified resampling strategy is applied. A comparative evaluation of the proposed method with the adaptive genetic particle filter (AGPF) and the firefly algorithm intelligence optimized particle filter (FAIOPF) is conducted. Some results of the simulation and the practical experiment are presented; they indicate the proposed method offers accuracy improvements in the liquid-level estimation during the silicon crystal growth. More specifically, compared with the AGPF and the FAIOPF, the mean absolute error (MAE) of the proposed method has been reduced by approximately 53.3% and 99.5%, respectively.

RF-SML: A SAR-Based Multi-Granular and Real-Time Localization Method for RFID Tags

With the rapid development of the Internet of Things (IoT) technology, location based service in context awareness has received increasing attention. As one of the main localization technologies, UHF RFID technology has been widely used in many fields of life and industry due to its advantages. In this article, we introduce a RFID-based system RF-SML, which is a method for quickly and accurately locating static objects via the tag and mobile reader. Specifically, the method utilizes the idea of multi-granularity in order to find the high-probability region of the target position by reconstructing the reflection coefficient of the scene in the coarse-grained localization stage. Subsequently, in the fine-grained localization stage, the grid is traversed in this area to calculate the corresponding evaluation factor to determine the final position result, thereby reducing the time-consuming of localization calculation. At the same time, it uses phase calibration to remove the phase offsets that are caused by the hardware device and the antenna phase center, thereby obtaining higher localization accuracy. We conduct experiments to verify the performance of RF-SML with commercial-off-the-shelf (COTS) RFID equipment. The results show that the proposed method can efficiently achieve the centimeter-level positioning of objects.

Sbi: A toolkit for simulation-based inference

Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging. Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. Importantly, SBI does not seek to recover a single 'best' data-compatible parameter set, but rather to identify all high probability regions of parameter space that explain observed data, and thereby to quantify parameter uncertainty. In Bayesian terminology, SBI aims to retrieve the posterior distribution over the parameters of interest. In contrast to conventional Bayesian inference, SBI is also applicable when one can run model simulations, but no formula or algorithm exists for evaluating the probability of data given parameters, i.e. the likelihood. We present $\texttt{sbi}$, a PyTorch-based package that implements SBI algorithms based on neural networks. $\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.

A search for optical and near-infrared counterparts of the compact binary merger GW190814

AbstractWe report on our observing campaign of the compact binary merger GW190814, detected by the Advanced LIGO and Advanced Virgo detectors on August 14th, 2019. This signal has the best localisation of any observed gravitational wave (GW) source, with a 90% probability area of 18.5 deg2, and an estimated distance of ≈240 Mpc. We obtained wide-field observations with the Deca-Degree Optical Transient Imager (DDOTI) covering 88% of the probability area down to a limiting magnitude of w = 19.9 AB. Nearby galaxies within the high probability region were targeted with the Lowell Discovery Telescope (LDT), whereas promising candidate counterparts were characterized through multi-colour photometry with the Reionization and Transients InfraRed (RATIR) and spectroscopy with the Gran Telescopio de Canarias (GTC). We use our optical and near-infrared limits in conjunction with the upper limits obtained by the community to constrain the possible electromagnetic counterparts associated with the merger. A gamma-ray burst seen along its jet’s axis is disfavoured by the multi-wavelength dataset, whereas the presence of a burst seen at larger viewing angles is not well constrained. Although our observations are not sensitive to a kilonova similar to AT2017gfo, we can rule out high-mass (> 0.1 M⊙) fast-moving (mean velocity ≥ 0.3c) wind ejecta for a possible kilonova associated with this merger.

Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations

Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location () as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source’s location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.

Incorporating expert prior in Bayesian optimisation via space warping

Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. Since the function evaluations are expensive in terms of both money and time, it may be desirable to alleviate this problem. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In its standard form, Bayesian optimisation assumes the likelihood of any point in the search space being the optimum is equal. Therefore any prior knowledge that can provide information about the optimum of the function would elevate the optimisation performance. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum. We incorporate this prior directly in function model (Gaussian process), by redefining the kernel matrix, which allows this method to work with any acquisition function, i.e. acquisition agnostic approach. We show the superiority of our method over standard Bayesian optimisation method through optimisation of several benchmark functions and hyperparameter tuning of two algorithms: Support Vector Machine (SVM) and Random forest.

Improved Flooding Molecular Dynamics Analysis

Many proteins, regardless of their function, can be regulated by a myriad of small molecules. Knowing the details of these interactions in molecular scale is a key point to understand the mechanism of biological processes. However, protein modulation by small ligands have complex features: there may exist multiple binding sites, with different occupancy states, many ligand configurations and varying affinities. A good description of these phenomena can be obtained by flooding Molecular Dynamics (flooding-MD), which is a simulation with a complete protein system added a fixed number of ligands. Usually, the main information obtained are high-probability ligand regions nearby the receptor. However, truthful results require long computational timescales due to convergence of a huge number of states. Given the effort put into these simulations, here we present an optimized framework to analyze flooding-MD simulations, capable of reconstructing ligand binding constants, which will be able to describe several interaction properties in any desired concentration - occupancy probabilities, spatial probabilities, mean bound ligand numbers and site affinities - without the need to run new simulations. We illustrate the use of the framework for the mammalian voltage-gated potassium channel Kv1.2, and sevoflurane, a positive modulator. For each concentration ranging from 50mM to 150mM sevoflurane, >200ns flooding-MD simulations were performed. From the highest concentrated simulation (150mM), for each site, the theory was applied to estimate the binding constants and other properties at more diluted concentrations. In order to verify accuracy of these projections, they were directly compared to more diluted flooding-MD simulations (<150mM sevoflurane) properties and had satisfying results, within an error margin. This improved way of analyzing flooding-MDs can be applied to diverse protein-ligand interactions, offering a simpler and faster way to study these interactions, without the need of additional simulations.

Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess HE lesions accurately, we propose a novel automatic HE lesion segmentation and classification network that contains lesion region positioning (LRP) and lesion region segmenting (LRS) modules. First, we used the LRP module to obtain the probability map of the lesion distribution and the position of the lesion. Then, based on the result of the LRP module, we used the LRS module to precisely segment the HE lesions within the high-probability region. Finally, we classified the HE lesions and identified the lesion types by a convolutional neural network (CNN). The entire dataset was delineated by the hospital's senior radiologist. We collected CT slices of 160 patients from Qinghai Provincial People's Hospital. The Dice score of the final segmentation result reached 89.89%. The Dice scores, indicating the classification accuracy, for cystic vs. alveolar echinococcosis and calcified vs. noncalcified lesions were 80.32% and 82.45%, the sensitivities were 72.41% and 75.17%, the specificities were 83.72% and 86.04%, the NPVs were 80.01% and 86.96%, the PPVs were 80.45% and 81.74%, and the areas under the ROC curves were 0.8128 and 0.8205, respectively. Graphical abstract.

Noise can speed Markov chain Monte Carlo estimation and quantum annealing.

Carefully injected noise can speed the average convergence of Markov chain Monte Carlo (MCMC) estimates and simulated annealing optimization. This includes quantum annealing and the MCMC special case of the Metropolis-Hastings algorithm. MCMC seeks the solution to a computational problem as the equilibrium probability density of a reversible Markov chain. The algorithm must cycle through a long burn-in phase until it reaches equilibrium because the Markov samples are statistically correlated. The special injected noise reduces this burn-in period in MCMC. A related theorem shows that it reduces the cooling time in simulated annealing. Simulations showed that optimal noise gave a 76% speed-up in finding the global minimum in the Schwefel optimization benchmark. The noise-boosted simulations found the global minimum in 99.8% of trials compared with only 95.4% of trials in noiseless simulated annealing. Simulations also showed that the noise boost is robust to accelerated cooling schedules and that noise decreased convergence times by more than 32% under aggressive geometric cooling. Molecular dynamics simulations showed that optimal noise gave a 42% speed-up in finding the minimum potential energy configuration of an eight-argon-atom gas system with a Lennard-Jones 12-6 potential. The annealing speed-up also extends to quantum Monte Carlo implementations of quantum annealing. Noise improved ground-state energy estimates in a 1024-spin simulated quantum annealing simulation by 25.6%. The quantum noise flips spins along a Trotter ring. The noisy MCMC algorithm brings each Markov step closer on average to equilibrium if an inequality holds between two expectations. Gaussian or Cauchy jump probabilities reduce the noise-benefit inequality to a simple quadratic inequality. Simulations show that noise-boosted simulated annealing is more likely than noiseless annealing to sample high probability regions of the search space and to accept solutions that increase the search breadth.

Convergence complexity analysis of Albert and Chib’s algorithm for Bayesian probit regression

The use of MCMC algorithms in high dimensional Bayesian problems has become routine. This has spurred so-called convergence complexity analysis, the goal of which is to ascertain how the convergence rate of a Monte Carlo Markov chain scales with sample size, $n$, and/or number of covariates, $p$. This article provides a thorough convergence complexity analysis of Albert and Chib’s [J. Amer. Statist. Assoc. 88 (1993) 669–679] data augmentation algorithm for the Bayesian probit regression model. The main tools used in this analysis are drift and minorization conditions. The usual pitfalls associated with this type of analysis are avoided by utilizing centered drift functions, which are minimized in high posterior probability regions, and by using a new technique to suppress high-dimensionality in the construction of minorization conditions. The main result is that the geometric convergence rate of the underlying Markov chain is bounded below 1 both as $n\rightarrow\infty$ (with $p$ fixed), and as $p\rightarrow\infty$ (with $n$ fixed). Furthermore, the first computable bounds on the total variation distance to stationarity are byproducts of the asymptotic analysis.

Time-scaled phylogeography of complete Zika virus genomes using discrete and continuous space diffusion models

Zika virus (ZIKV), a vector-borne infectious agent that has recently been associated with neurological diseases and congenital microcephaly, was first reported in the Western hemisphere in early 2015.A number of authors have reconstructed its epidemiological history using advanced phylogenetic approaches, and the majority of Zika phylogeography studies have used discrete diffusion models. Continuous space diffusion models make it possible to infer the possible origin of the virus in real space by reconstructing its ancestral location on the basis of geographical coordinates deduced from the latitude and longitude of the sampling locations. We analysed all the ZIKV complete genome isolates whose sampling times and localities were available in public databases at the time the study began, using a Bayesian approach for discrete and continuous phylogeographic reconstruction.The discrete phylogeographic analysis suggested that ZIKV emerged to become endemic/epidemic in the first decade of the 1900s in the Ugandan rainforests, and then reached Western Africa and Asia between the 1930s and 1950s. After a long period of about 40 years, it spread to the Pacific islands and reached Brazil from French Polynesia. Continuous phylogeography of the American epidemic showed that the virus entered in north-eastern Brazil in late 2012 and started to spread in early 2013 from two high probability regions: one corresponding to the entire north-east Brazil and the second surrounding the city of Rio de Janeiro, in a mainly northwesterly direction to Central America, the north-western countries of south America and the Caribbean islands. Our data suggest its cryptic circulation in both French Polynesia and Brazil, thus raising questions about the mechanisms underlying its undetected persistence in the absence of a known animal reservoir, and underline the importance of continuous diffusion models in making more reliable phylogeographic reconstructions of emerging viruses.

Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs

Markov chain Monte Carlo (MCMC) methods require a large number of samples to approximate a posterior distribution, which can be costly when the likelihood or prior is expensive to evaluate. The number of samples can be reduced if we can avoid repeated samples and those that are close to each other. This is the idea behind deterministic sampling methods such as quasi-Monte Carlo (QMC). However, the existing QMC methods aim at sampling from a uniform hypercube, which can miss the high probability regions of the posterior distribution and thus the approximation can be poor. Minimum energy design (MinED) is a recently proposed deterministic sampling method, which makes use of the posterior evaluations to obtain a weighted space-filling design in the region of interest. However, the existing implementation of MinED is inefficient because it requires several global optimizations and thus numerous evaluations of the posterior. In this article, we develop an efficient algorithm that can generate MinED samples with few posterior evaluations. We also make several improvements to the MinED criterion to make it perform better in high dimensions. The advantages of MinED over MCMC and QMC are illustrated using an example of calibrating a friction drilling process.

Statistical learning of anomalous regions in complex faux X-ray images does not transfer between detection and discrimination

The visual environment contains predictable information - “statistical regularities” - that can be used to aid perception and attentional allocation. Here we investigate the role of statistical learning in facilitating search tasks that resemble medical-image perception. Using faux X-ray images, we employed two tasks that mimicked two problems in medical-image perception: detecting a target signal that is poorly segmented from the background; and discriminating a candidate anomaly from benign signals. In the first, participants searched a heavily camouflaged target embedded in cloud-like noise. In the second, the noise opacity was reduced, but the target appeared among visually similar distractors. We tested the hypothesis that learning may be task-specific. To this end, we introduced statistical regularities by presenting the target disproportionately more frequently in one region of the space. This manipulation successfully induced incidental learning of the target’s location probability, producing faster search when the target appeared in the high-probability region. The learned attentional preference persisted through a testing phase in which the target’s location was random. Supporting the task-specificity hypothesis, when the task changed between training and testing, the learned priority did not transfer. Eye tracking showed fewer, but longer, fixations in the detection than in the discrimination task. The observation of task-specificity of statistical learning has implications for theories of spatial attention and sheds light on the design of effective training tasks.

Efficient coarser‐to‐fine holistic traffic sign detection for occlusion handling

In this study, the authors present a new efficient method based on discriminative patches (d-patches) for holistic traffic sign detection with occlusion handling. Traffic sign detection is an important part in autonomous driving, but usually hampered by the occlusions encountered on roads. They propose a method which basically upgrades d-patches by integrating vocabulary learning features. Consequently, d-patches are more discriminatively trained for robust occlusion handling. In addition, a holistic classifier is trained on d-patches, which identify those regions where occlusion exists. This results in higher confidence-score for the regions which contain traffic signs and lower confidence-score for the regions containing occlusions. Furthermore, they also propose a new coarser-to-fine (CTF) approach to speed up the traffic sign detection process. CTF minimises the use of traditional sliding window for object detection. It relies on colour variance to search the regions with high probability of traffic sign presence. Sliding window is used only on the selected high probability regions. The proposed method achieves 100% detection results on German Traffic Sign Detection Benchmark and performs 2.2% better than the previous state-of-the-art methods on Korean Traffic Sign Detection dataset, under partially occluded settings. By using CTF approach, five times speedup with a marginal loss in accuracy can be achieved.

Magnetic Recording Using a Spin Torque Oscillator

Magnetic Recording Using a Spin Torque Oscillator

Coverage-based cooperative target acquisition for hypersonic interceptions

This paper presents a novel coverage-based cooperative target acquisition algorithm for hypersonic interceptions. Firstly, the difficulties in the hypersonic trajectory prediction are introduced which invalidate the conventionally used predicted impact point based mid-course guidance and seeker acquisition. Secondly, in order to optimally estimate and predict the target trajectory information, the interacting multiple model (IMM) algorithm is used with the constant velocity (CV) model, the constant acceleration (CA) model and the Singer model serving as the model set. The target states are described with the probability density function (PDF) based on the IMM prediction. Thirdly, the interceptor seeker target acquisition model is established which considers the blur edge region of the field of view. The cooperative target acquisition algorithm is designed by maximizing the interceptor seekers cooperative coverage of the target high probability region (HPR). Finally, digital simulations prove the effectiveness of the proposed method and reveal that the real challenge in the hypersonic target acquisition is the poor trajectory prediction accuracy which may further result to the unsteadiness of the interceptor trajectories.