Efficient Sampling Technique Research Articles

Efficient Parametrization of Transferable Atomic Cluster Expansion for Water.

We present a highly accurate and transferable parametrization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of the ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab initio molecular dynamics simulations. Our results demonstrate that the ACE descriptors enable a potential initially fitted solely on ice structures, which is later upfitted with few configurations of liquid, identified with AL to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient, and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations while also presenting a transferable interatomic potential for water that reveals the accuracy of first-principles reference. This advancement not only enhances our understanding of the relationship between ice and liquid water at the atomic level but also opens up new avenues for studying complex aqueous systems.

Continual few-shot patch-based learning for anime-style colorization

The automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.

Precision enhancement in variance estimation for complex environmental populations using adaptive cluster sampling

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

Efficient and Reliable Estimation of Knowledge Graph Accuracy

Data accuracy is a central dimension of data quality, especially when dealing with Knowledge Graphs (KGs). Auditing the accuracy of KGs is essential to make informed decisions in entity-oriented services or applications. However, manually evaluating the accuracy of large-scale KGs is prohibitively expensive, and research is focused on developing efficient sampling techniques for estimating KG accuracy. This work addresses the limitations of current KG accuracy estimation methods, which rely on the Wald method to build confidence intervals, addressing reliability issues such as zero-width and overshooting intervals. Our solution, rooted in the Wilson method and tailored for complex sampling designs, overcomes these limitations and ensures applicability across various evaluation scenarios. We show that the presented methods increase the reliability of accuracy estimates by up to two times when compared to the state-of-the-art while preserving or enhancing efficiency. Additionally, this consistency holds regardless of the KG size or topology.

Supplementary Influence Maximization Problem in Social Networks

Due to important applications in viral marketing, influence maximization (IM) has become a well-studied problem. It aims at finding a small subset of initial users so that they can deliver information to the largest amount of users through the word-of-mouth effect. The original IM only considers a singleton item. And the majority of extensions ignore the relationships among different items or only consider their competitive interactions. In reality, the diffusion probability of one item will increase when users adopted supplementary products in advance. Motivated by this scenario, we propose a supplementary independent cascade (IC) and discuss the supplementary IM problem. Our problem is NP-hard, and the computation of the objective function is #P-hard. We notice that the diffusion probability will change when considering the impact of its supplementary product. Therefore, the efficient reverse influence sampling (RIS) techniques cannot be applied to our problem directly even though the objective function is submodular. To address this issue, we utilize the sandwich approximation (SA) strategy to obtain a data-dependent approximate solution. Furthermore, we define the supplementary-based reverse reachable (SRR) sets and then propose a heuristic algorithm. Finally, the experimental results on three real datasets support the efficiency and superiority of our methods.

Hydrogen bond network structures of protonated 2,2,2-trifluoroethanol/ethanol mixed clusters probed by infrared spectroscopy combined with a deep-learning structure sampling approach: the origin of the linear type network preference in protonated fluoroalcohol clusters.

While preferential hydrogen bond network structures of cold protonated alcohol clusters H+(ROH)n are generally switched from a linear type to a cyclic one at n = 4-5, those of protonated 2,2,2-trifluoroethanol (TFE) clusters maintain linear type structures at least in the size range of n = 3-7. To explore the origin of the strong linear type network preference of H+(TFE)n, infrared spectra of protonated mixed clusters H+(TFE)m(ethanol)n (m + n = 5) were measured. An efficient structure sampling technique using parallelized basin-hopping algorithms and deep-learning neural network potentials is developed to search for essential isomers of the mixed clusters. Vibrational simulations based on the harmonic superposition approximation were compared with the observed spectra to identify the major isomer component at each mixing ratio. It was found that the formation of the cyclic structure occurs only in n ≥ 3 of the mixed clusters, in which the proton solvating sites and the double acceptor site are occupied by ethanol. The crucial role of the stability of the double acceptor site in the cyclic structure formation is discussed.

Efficient sampling techniques for surrogate-based optimization with thermoelastic application

In many real-life models, the objective function may be very expensive to calculate or sometimes a black-box function. Therefore, a surrogate model is required to reduce the cost of the calculations. To build a robust surrogate model, a good sampling technique is needed. This paper suggests two new sampling techniques for surrogate-based optimization, depending on four random walk steps, which in turn give a good chance for building an accurate approximation for the original function. In addition to that, one of these new sampling techniques has a new feature that was not used before. The main advantage of our two suggested sampling techniques is that there is no optimization subproblem will be solved for the next sample point, which in turn saves CPU time. Furthermore, we applied our two sampling techniques to a 1D thermal shock problem for a half-space in the context of the fractional order theory of thermoelasticity to reduce the number of function evaluations and consequently CPU time. Laplace transform techniques are used to obtain the solution of the system in the frequency domain. Applying our algorithms to the few points obtained by using Laplace’s inverse, we can reach the optimal solution for all functions filed. The predictions of our techniques are discussed and compared with the classical method. 2D and 3D graphs are plotted for our numerical results. The temperature, displacement, and stress distributions are computed and represented graphically for different times and several fractional parameters.

"Total Human DNA Sampling" – Forensic DNA profiles from large areas

Employed for the first time in 1986, DNA profiling is nowadays established as one of the most widely used forensic techniques worldwide. However, until today, no efficient sampling technique existed to collect DNA from human skin cells from a large area, not to say from the floor of an entire room. This has been extremely unfortunate, as there is enormous forensic potential in these DNA traces from the ground to provide clues as to who has been present at a particular location, i.e. at the crime scene. By desquamation, humans loose several millions of skin cells per day, everywhere they stand, sit or walk; and they can do little about it. We developed a fast and simple method by which we can make use of all those lost skin cells. We use a vacuum cleaner equipped with a specialized filter cartridge to sample the ground. Fragmentation of the filter membrane and subsequent parallel processing of the filter fragments, using a modified Chelex® 100 extraction protocol, significantly reduce the complexity of the dust mixture. In this way, a large number of interpretable major contributor DNA profiles can be generated from individuals who have been present on the sampled surface. Overall, at least 38 % of the generated DNA profiles from all sampled test areas fulfilled the criteria for submission of single major contributor profiles to the Swiss DNA database. As demonstrated through a mock crime scene scenario simulating an indoor stabbing event, the perpetrator's DNA could be found on the floor even after a very short stay in the room of less than one minute. Furthermore, already the first application of the method at a real crime scene led to relevant case information for the police. Given its large investigative potential, we recommend Total Human DNA Sampling as a helpful complemental forensic tool to conventional DNA trace collection in major crimes.

An automatic assisted drill system for sampling deep layer regolith of extraterrestrial celestial bodies

As one of the most important parts of deep space exploration missions, the sampling technologies of extraterrestrial celestial bodies (ECBs) have attracted the attention and interest of many researchers. As a relatively efficient and convenient sampling technique, drill samplers have been applied in different ECBs exploration missions. Different from the previous drill apparatus, this paper introduces a novel automatic assisted drill system (AADS), which is used to sample the deep layer regolith of ECBs. This drilling system can sample the deep regolith layer under manual assistance or unmanned automatic operation mode. The sampler has improved and designed a segmented detachable coring pipe, drilling pipe and casing pipe. It can largely maintain the layer information in the depth direction of the ECBs regolith and can also automatically change the drill sampling process through the driller change system to improve the sampling efficiency. Finally, an automatic test apparatus was designed, and an experimental platform was established to conduct a drill sampling test for the AADS sampler. The experimental results show that the AADS sampling system can successfully obtain at least 2.4 m depth and 1.1 kg weight simulated regolith samples.

Acoustic Full Waveform Inversion with Hamiltonian Monte Carlo Method

Full-Waveform Inversion (FWI) is a high-resolution technique used in geophysics to evaluate the physical parameters and construct subsurface models in a noisy and limited data scenario. The ill-posed nature of the FWI turns this a challenging problem since more than one model can match the observations. In a probabilistic way, solving the FWI problem demands efficient sampling techniques to infer information on parameters and to estimate the uncertainties in high-dimensional model spaces. We investigate the feasibility of applying the Hamiltonian Monte Carlo (HMC) method in the acoustic FWI by a reflection setup containing different noise level data. We propose a new strategy for tuning the mass matrix based on the acquisition geometry of the seismic survey. Our methodology significantly improves the ability of the HMC method in reconstructing reasonable seismic models with affordable computational efforts.

A comparison of four sampling techniques for assessing species richness of adult odonates at riverbanks

Members of the insect order Odonata are known as good ecological indicators. Many are sensitive to habitat modifications and are easily monitored for use in environmental assessment studies. Rapid assessments rely on efficient sampling techniques. However, there is limited information available on sampling techniques for adult odonates, and protocols require evaluation. To do this, we standardized counting methods during sampling of odonates from August to November 2016 at the Mula River, Pune, India. We used four counting techniques; full-width belt transect (FWBT), full-circle point count (FCPC), half-width belt transect (HWBT), and half-circle point count (HCPC). For HWBT and HCPC areas facing the river were sampled, and for each technique we took multiple temporal replicates. We compared species detected per unit time, species detected per unit area, new species detected per unit time, and new species detected per unit area. Additionally, we compared species estimates. With HCPC we detected the maximum number of species and new species per unit area, whereas FWBT returned maximum coverage of recorded species. We recommend our proposed techniques be considered in the future across various habitats to decide the most suitable sampling strategy for the different habitats or situations.

Transient response of Bouc–Wen hysteretic system under random excitation via RBFNN method

Hysteresis normally exhibited by mechanical systems and materials is so prevalent that its response prediction under random excitation has been extensively investigated for decades. Nevertheless, the transient solution of the response, which is crucial for assessing the system’s reliability, is still a challenging topic that requires additional development. To this regard, this work proposes a semi-analytical method using the radial basis function neural network (RBFNN) to attain the transient probability density distribution of the randomly excited Bouc–Wen system. Specifically, the trial solution of the corresponding FPK equation is configured as the RBFNN with undetermined time-varying weight coefficients. By discretizing the time derivative with the Euler difference method, a loss function with time recurrence is derived and minimized to yield the time-varying optimal weight coefficients through the optimization method. Additionally, an optimized sampling strategy is adopted to reduce the burden of calculation. Finally, the Bouc–Wen hysteretic systems with softening and hardening nonlinearity are considered to investigate the performance of the adopted technique. The numerical results have shown that the evaluation process of the probability density functions(PDFs) can be captured well with sufficient accuracy and efficiency. The proposed efficient sampling technique can provide considerable efficiency improvement for the medium dimension system. The work of the paper will contribute to the reliability design of hysteretic structures in engineering.

Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars.

Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (Citrus reticulata Blanco) and orange (Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus.

Sequential Monte Carlo optimization and statistical inference

AbstractSequential Monte Carlo (SMC) is a powerful technique originally developed for particle filtering and Bayesian inference. As a generic optimizer for statistical and nonstatistical objectives, its role is far less known. Density‐tempered SMC is a highly efficient sampling technique ideally suited for challenging global optimization problems and is implementable with a somewhat arbitrary initialization sampler instead of relying on a prior distribution. SMC optimization is anchored at the fact that all optimization tasks (continuous, discontinuous, combinatorial, or noisy objective function) can be turned into sampling under a density or probability function short of a norming constant. The point with the highest functional value is the SMC estimate for the maximum. Through examples, we systematically present various density‐tempered SMC algorithms and their superior performance vs. other techniques like Markov Chain Monte Carlo. Data cloning and k‐fold duplication are two easily implementable accuracy accelerators, and their complementarity is discussed. The Extreme Value Theorem on the maximum order statistic can also help assess the quality of the SMC optimum. Our coverage includes the algorithmic essence of the density‐tempered SMC with various enhancements and solutions for (1) a bi‐modal nonstatistical function without and with constraints, (2) a multidimensional step function, (3) offline and online optimizations, (4) combinatorial variable selection, and (5) noninvertibility of the Hessian.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods Algorithms and Computational Methods > Stochastic Optimization Algorithms and Computational Methods > Integer Programming

A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts

Extreme value theory is the state-of-the-art modelling technique for estimating crash risk from traffic conflicts, with two different sampling techniques, i.e. block maxima and peak-over-threshold, at its core. However, the uncertainty associated with the estimates obtained by these sampling techniques has been too large to enable its widespread practical use. A fundamental reason for this issue is the improper selection of extreme values and a lack of a suitable and efficient sampling mechanism. This study proposes a hybrid modelling framework of machine learning and extreme value theory to estimate crash risk from traffic conflicts with an efficient sampling technique for identifying extremes. More specifically, a machine learning approach replaces the conventional sampling techniques with anomaly detection techniques since an anomaly is a data point that does not conform with the rest of the data, making it very similar to the definition of an extreme value. Six representative machine learning-based unsupervised anomaly detection algorithms have been tested in this study. They include iforest, minimum covariance determinant, one-class support vector machine, k-nearest neighbours, local outlier factor, and connectivity-based outlier factor. The extremes identified by these algorithms are then fitted to extreme value distributions for both univariate and bivariate frameworks. These algorithms were tested on a large set of traffic conflict data collected for four weekdays (6 am to 6 pm) from three four-legged intersections in Brisbane, Australia. Results indicate that the proposed hybrid models consistently outperform the conventional extreme value models, which use block maxima and peak-over-threshold as the underlying sampling technique. Among the sampling algorithms, iforest has been found to perform better than other algorithms in estimating crash risks from traffic conflicts. The proposed hybrid modelling framework represents a methodological advancement in traffic conflict-based crash estimation models and opens new avenues for exploring the possibility of utilising machine learning techniques within the existing traffic conflict techniques.

Fast and Accurate Proper Orthogonal Decomposition using Efficient Sampling and Iterative Techniques for Singular Value Decomposition

In this article, we propose a computationally efficient iterative algorithm for proper orthogonal decomposition (POD) using random sampling based techniques. In this algorithm, additional rows and columns are sampled and a merging technique is used to update the dominant POD modes in each iteration. We derive bounds for the spectral norm of the error introduced by a series of merging operations. We use an existing theorem to get an approximate measure of the quality of subspaces obtained on convergence of the iteration. Results on various datasets indicate that the POD modes and/or the subspaces are approximated with excellent accuracy with a significant runtime improvement over computing the truncated SVD. We also propose a method to compute the POD modes of large matrices that do not fit in the RAM using this iterative sampling and merging algorithms.

Two-dimensional t−t′ Holstein model

The competition and interplay between charge-density wave and superconductivity have become a central subject for quasi-2D compounds. Some of these materials, such as the transition-metal dichalcogenides, exhibit strong electron-phonon coupling, an interaction that may favor both phases, depending on the external parameters, such as hydrostatic pressure. In view of this, here we analyze the single-band $t$-$t^{\prime}$ Holstein model in the square lattice, adding a next-nearest neighbor hopping $t^{\prime}$ in order to play the role of the external pressure. To this end, we perform unbiased quantum Monte Carlo simulations with an efficient inversion sampling technique appropriately devised for this model. Such a methodology drastically reduces the autocorrelation time, and increases the efficiency of the Monte Carlo approach. By investigating the charge-charge correlation functions, we obtain the behavior of the critical temperature as a function of $t^{\prime}$, and from compressibility analysis, we show that a first-order metal-to-insulator phase transition occurs. We also provide a low-temperature phase diagram for the model.

Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

Inverse analysis by finite element model (FEM) based on measured displacement data is a popular approach for parameter identification of concrete dams. FEM-based inverse analysis method is economical and effective, but with some limitations such as complex computation and time-consuming. This paper presents a kernel extreme learning machine (KELM)-based response surface model (RSM) for parameter inverse analysis of concrete dams. The FEM is replaced by KELM-based RSM to explore the relationships between material parameters and displacement response of dam-foundation systems. Sample data of KELM-based RSM are generated by the efficient sampling technique Latin hypercube sampling. Subsequently, a novel optimization algorithm Jaya is adopted to minimize the objective function for material parameters identification. The effectiveness and practicability of the proposed approach are verified by two cases and two real concrete gravity dams in operation with sufficient monitoring data. Comparative studies with direct finite element method and several optimization algorithms were also performed to demonstrate the superiority of the proposed approach over commonly used techniques. Results show that the proposed KELM-based RSM is a simple and efficient way to achieve high accuracy in parameter inverse analysis of concrete dams at a low computation cost.

Adaptive Hermite Distribution Model with Probability-Weighted Moments for Seismic Reliability Analysis of Nonlinear Structures

In this paper, an adaptive Hermite distribution model with probability-weighted moments (PWMs) is proposed for evaluating the extreme-value distribution (EVD) of response, which serves as the basis of seismic reliability analysis of complex nonlinear structures under random seismic excitations. From the perspective of EVD, the problem formulation is first introduced. Then, an adaptive distribution model, named as the adaptive Hermite polynomial normal transformation model (A-HPNT), is established to estimate the EVD. The undetermined coefficients of A-HPNT are specified via the PWMs matching technique, in which only linear systems of equations need to be solved. To optimally determine the degree for A-HPNT, a two-step criterion is effectively established accordingly. An efficient high-dimensional sampling technique is introduced for generating samples of extreme value, estimating both the PWMs and statistical moments of EVD. When the entire distribution of EVD is recovered, one can compute the failure probability and reliability index via an integral over the EVD. Two numerical examples, a 10-story nonlinear shear frame structure and a practical 13-story reinforced concrete frame-shear wall structure driven by random seismic excitations, are presented to verify the efficacy of the proposed method for seismic reliability evaluation of complex nonlinear structures.

Structural dynamic reliability analysis of super large-scale lattice domes during earthquakes using the stochastic finite element method

For large-scale dome structures subjected to earthquake ground motions, the need for accurate and efficient approaches that account for uncertainties in design, material properties, loads, damping, and manufacturing processes has grown significantly. In uncertainty analyses, the theory of probability, uncertainty quantification, and reliability analysis approaches are commonly used. However, because of computing efficiency and capability, the space scale of dome structures is severely limited. As a result, the computational issues of the complex dynamic reliability that super-large-scale dome structures encounter during earthquakes are challenging. The uncertainties of the dynamic demands of a super-large-scale lattice dome with multiple variables are quantified using a stochastic finite element method (SFEM) with nonlinear time-history analysis developed in this paper, and this method can consider the randomness of variables in more detail for a structure. The dynamic reliability is investigated in depth using the efficient Latin hypercube sampling (LHS) technique to reduce the number of repetitive time-consuming calculations. To improve the efficiency and accuracy of the analysis, an optimization approach based on the genetic algorithm (GA) is used to obtain the probability of structural failure and the reliability index. The results show that these methods are highly efficient for super-large-scale structures. Finally, a sensitivity analysis based on variable randomness is conducted to further evaluate the effects of variables on structural performance using various evaluation methods. This paper provides an efficient solution for structural dynamic reliability problems of super-large-scale structures in the SFEM framework.