Sample Space Research Articles

Spot Invalid Point Repair Algorithm of Detector Array Measurement System Based on Image Correlation Coefficient

The detector array method has been widely used in the field of high-energy laser far-field spot parameter measurement due to its ability to directly measure the far-field spot of high-energy lasers, wide dynamic range of the detectors, high system sampling frequency, good real-time performance, and suitability for various testing environment requirements. However, during the measurement process, the irradiation of strong lasers or damage to other hardware systems can result in invalid points in the acquired spot images, thereby reducing the measurement accuracy of the system. In order to achieve accurate measurement of laser far-field spot parameters, this paper establishes an experimental model based on the analysis of the sampling spacing of the detector array target and proposes a laser far-field spot invalid point repair algorithm based on image correlation coefficient. Experimental results demonstrate that the algorithm proposed in this paper effectively reduces the impact of invalid points in the measurement system on the measurement accuracy, and achieves accurate measurement of high-energy laser measurement systems.

Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering

Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.

Perceptual cue-guided adaptive image downscaling for enhanced semantic segmentation on large document images

AbstractImage downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both content-independent and content-aware strategies. The approach limits the sampling space per the content-independent strategy, adaptively relocating such sampled pixel points, and amplifying their intensities based on the local gradient and texture via the content-aware strategy. To demonstrate its effectiveness, we plug our adaptive downscaling method into a deep learning-based document image segmentation pipeline and evaluate the performance improvement. We perform the evaluation on three publicly available historical newspaper digital collections with differences in quality and quantity, comparing our method with one widely used downscaling method, Lanczos. We further demonstrate the robustness of the proposed method by using three different training scenarios: stand-alone, image-pyramid, and augmentation. The results show that training a deep convolutional neural network using images generated by the proposed method outperforms Lanczos, which relies on only content-independent strategies.

Physical Properties of Hydrocarbon Source Reservoir in the Lucaogou Formation in Junggar Basin (China) and Its Influence on Adsorption Ability and Surface Free Energy

A physical property of a shale gas reservoir affects shale gas content, then restricts the shale gas resource potential. In this paper, lithofacies and spatial distribution of the southern margin of the Junggar Basin in Xinjiang Province are identified and occurrence strata, gas content, and reservoir properties of shale gas are studied. Based on adsorption potential theory, adsorption and surface free energy of all the sample is discussed. The conclusions are as follows. (1) All the shale samples can be divided into five lithological phases. For example, black oil shale/shale (lithology) phase and gray-black-gray/dolomite mudstone (lithology) phase are the most developed; compared with the middle and lower sections, the vertical development continuity of the upper hydrocarbon source rock is better. Lithology of this section is mainly composed of shale mixed with marlstone and dolomite interlayer. From the horizontal view of this section, the overall trend is gradually thickening from west to east and from north to south. (2) Semi-deep lake-phase is the most developed, indicating a decreasing trend of thickness from sedimentary center to surrounding strata. (3) Sedimentary period of Lucaogou Formation is a deep water area of the lake basin; then, the TOC content is affected by the sedimentary environment. As sedimentary water depth increases, TOC content will increase, which results in the highest TOC content in the area. A specific surface area is roughly positively correlated with porosity, clay mineral content, and percentage of illite/montmorillonite interlayer ratio and negatively correlated with TOC content. (4) During the adsorption process, adsorption potential decreases with an increase in equilibrium pressure, and adsorption space increases with an increase in equilibrium pressure. Maximum adsorption space of all the sample were 0.20~0.25 cm3·g−1; then, its value is larger than the maximum adsorption space of other coal samples (0.025~0.20 cm3·g−1). In the same adsorption space, the corresponding adsorption potential of the coal sample is much larger than that of other samples. The reason is that the porosity permeability of this sample is relatively larger, leading to its better physical properties.

Non-destructive characterization of cylindrical shells by probing: Identification of the probing location from energy barrier and probing forces using Measured Geometric Imperfections (MGI)

In the present study, the prediction of the critical buckling load of an axially compressed cylindrical shell is investigated based on the novel non-destructive probing method with the aid of Measured Geometric Imperfections (MGI). A laser point scanner is used to obtain the imperfection data. The imperfection data is analyzed to find a sample space of points where the buckling is likely to initiate. The optimum location for probing is determined from the sample set by finding the Least Resistance Path (LRP) to probing using two methods, one based on the Energy barrier and the other from the reaction forces observed corresponding to deep probe advancements. It is found that MGI can aid the procedure by improving the selection of the probing points and hence the prediction accuracy; However, MGI does not eliminate the need for multiple probing. The study has shown the possibility of obtaining multiple probing locations that can predict the peak load within acceptable levels of accuracy.

A two-stage Bayesian model updating framework based on an iterative model reduction technique using modal responses

A two-stage Bayesian model updating framework based on an affine-invariance sampling in Transitional Markov Chain Monte Carlo (TMCMC) algorithm is proposed. In the first stage, unknown modal coordinates are identified with an improved iterative model reduction technique. Subsequently, a modified TMCMC algorithm is proposed where obtaining the statistical estimators in the usual TMCMC algorithm is not necessary. In detail, an affine-invariance sampling approach is proposed to estimate a multi-dimensional scaling (MDS) factor in the affine-transformed space that accounts for the frequencies and mode shapes which are of different natures and dimensions at the levels of likelihood, prior and posterior distributions. The TMCMC sampling adaptively utilizes the plausibility value from the proposed MDS-based tuning algorithm to improve its transition levels. The proposed algorithm in the affine-invariance sampling space accounting for the observables of different dimensions is expected to facilitate the estimation of the posterior distribution of model parameters. The effectiveness of the proposed algorithm is demonstrated by considering a simply-supported steel beam, a ten-storied reinforced concrete building model and an eight-degree-of-freedom spring–mass model for which experimental data is available. The second example demonstrates the applicability of the approach for updating large finite element models involving substructuring.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings, the detection methods using acceleration sensors suffer from signal aliasing, while magnetic detection methods are susceptible to interference from ferromagnetic materials, thereby posing challenges in accurately determining the number of layers. To address this issue, this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion, utilizing both the temporal convolutional network (TCN) and the long short-term memory (LSTM) recurrent network. By leveraging the strengths of these two network structures, the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process, establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate. A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates, capturing the multi-source physical field signals and their patterns during the penetration process. The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting, respectively. The model's predictive performance is evaluated under various operating conditions, including different ratios of added noise to random sample positions, penetration speeds, and spacing between target plates. The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms, 0.12 ms, and 0.16 ms, respectively, confirming the robustness of the proposed model. Moreover, the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Generative adversarial minority enlargement—A local linear over-sampling synthetic method

The imbalanced data problem is widely recognized in real-world datasets. To avoid learning bias on imbalanced data, the over-sampling strategy is well studied and adopted for generating synthetic minority samples. Wherein, the Synthetic Minority Over-sampling Technology and its improved algorithms become standard baselines. In recent years, the popular Generative Adversarial Networks and its enhanced variants, introduced from the computer vision community, are believed to generate better samples, by approximating the true data distribution. Yet, we notice that the synthetic samples for the minority category in these existing methods is usually restrained in a limited samples space known in advance, which may mislead the classifiers trained on them to take data in the unsampled region of the minority category as from the majority category. Given such limitations, we propose a Generative Adversarial Minority Enlargement (GAME) method to intentionally extend the sampling margin during data generative and adversarial phases. This is accomplished by adjusting the parameters of a local linear model to approach the majority category. We conduct an extensive evaluation on 28 datasets of different domains, extracted from the UCI real-world datasets. The results show that GAME can achieve more balanced and stable results efficiently than 18 state-of-the-art methods.

Semantic-enhanced Contrastive Learning for Session-based Recommendation

Session-based recommendation aims to predict the next clicked item based on the short-term behavior sequence of an anonymous user, which is a challenging task owing to data sparsity. Although contrastive learning has been used extensively to address this problem, existing methods generally consider all other sessions in the mini-batch as negative samples, leading to a limited negative sample space and a failure to distinguish real negative samples from false negative samples. Thus, Semantic-enhanced Contrastive Learning for Session-based Recommendation (SCLRec) is proposed in this study. Specifically, a queue is designed to store session samples, and a momentum encoder is used to ensure high consistency in the large-capacity sample space. Furthermore, a novel semantic-enhanced mechanism is devised to filter out false negative samples and increase the weights of high-confidence negative samples according to semantic similarity scores between sessions, effectively reducing noise and enhancing contrastive learning. Moreover, the gated attention unit is used as the encoder to obtain excellent performance and efficiency compared with traditional attention networks. Extensive experiments on three real-world public datasets demonstrate that the proposed method achieves state-of-the-art performance with a relatively low time complexity.

A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical microscopy images

Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: (1) how to learn robust representations in an unsupervised manner from unlabeled biomedical microscopy images of low diversity in samples? and (2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework (TOWER) towards enhanced recognition of biomedical microscopy images, which works in three phases by synergizing contrastive learning and generative learning methods: (1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; (2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; (3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Experiments have been conducted on public datasets of biomedical microscopy images against the state-of-the-art counterparts (e.g., SimCLR and BYOL), and results demonstrate that: TOWER statistically excels in all self-supervised methods, achieving a Dice improvement of 1.38 percentage points over SimCLR. TOWER also has potential in multi-modality medical image analysis and enables label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.

Data-driven physical fields reconstruction of supercritical-pressure flow in regenerative cooling channel using POD-AE reduced-order model

In order to effectively estimate the physical fields of active cooling channel with supercritical pressure hydrocarbon fuel, a novel data-driven reduced-order model framework is firstly proposed in the present work, which is established via combining reduced-order modeling method based on proper orthogonal decomposition and autoencoder neural network (POD-AE) with Kriging surrogate model. This framework mainly contains offline stage and online stage. Preliminary dimension reduction and feature capture of high-dimensional physical fields data is conducted using POD in the offline stage, and low-dimensional reduced-order models (ROMs) are constructed by the linear combination of orthogonal bases and feature coefficients. Specially, feature coefficients of ROMs are used to trained an AE, and then lower-dimensional secondary reduced-order models (2nd-ROMs) are established by secondary compression for ROMs using the trained AE. Furthermore, the Kriging model is trained for implicitly mapping boundary conditions or temperature data from the sensors on the bottom surface to the established 2nd-ROMs. In the online stage, physical fields estimation under new boundary conditions or wall temperature monitoring based on temperature data of sensors can be quickly fulfilled using this framework. Fifty groups of test conditions are considered to demonstrate the accuracy and efficiency of the POD-AE ROM framework. The results prove that it shows good efficiency, accuracy in estimating and monitoring the physical fields of the cooling channel with supercritical pressure n-dodecane within sample space. Compared with the traditional POD-based ROM framework, the proposed framework still achieved acceptable accuracy under all testing conditions when the original data was compressed to extremely low dimensions. And the calculation time only slightly increased.

Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks

Machine learning methods can be used to predict the properties of materials from their structure. This can be particularly useful in cases where other standard methods for finding material properties are time and resources consuming to use on large sample spaces. In this work, we study the strength of α-quartz crystals with a porous layer created by simplex noise as the shape of porosity. We train a neural network to predict the yield stress of these systems under both shear and tensile deformation. Molecular dynamics simulations are used for a randomly selected sample of possible structures in order to generate the ground truth to be used as the training data. We employ deep convolutional neural networks (CNNs) which are commonly used when dealing with image or image-like data since the input data for the problem in hand are a binary 2D structure of the porous layer of the systems. The trained CNN can predict the yield stress of a system based on the geometry of that given system, with much higher precision compared to a baseline polynomial regression method. Saliency maps created with the trained model show that the model predictions are most sensitive to altering structures near high-stress regions, indicating that the model makes predictions based on reasonable physics.

Data-driven multi-stage sampling strategy for a three-dimensional geological domain using weighted centroidal voronoi tessellation and IC-XGBoost3D

Conventional site investigation schemes typically use an empirical sampling strategy, which involves equal sampling spacing with regular grid patterns. However, this approach assigns equal importance to the entire site and does not account for subsurface stratigraphic variations and site constraints. In this study, a data-driven multi-stage sampling strategy is proposed to adaptively optimize the borehole locations for a three-dimensional (3D) geotechnical site, considering the subsurface stratigraphic uncertainties and irregular site geometries. The initial sampling plan is determined based on weighted centroidal Voronoi tessellation, which assigns various sampling densities to zones depending on their importance. Measurements obtained in the previous sampling stage are combined with prior geological knowledge to establish and update a 3D geological domain using a physics-informed geological modelling method. The next optimal location for sampling is adaptively determined with the objective of maximizing the reduction in the stratigraphic uncertainty. The proposed method represents the first data-driven 3D site planning method that explicitly considers 3D subsurface stratigraphic variations. The performance of the proposed multi-stage sampling strategy is illustrated using a simulation study. The results indicate that the proposed method efficiently identifies the optimal sampling locations while comprehensively considering the 3D subsurface geological uncertainties and irregular site geometries.

Uniform probability in cosmology

Problems with uniform probabilities on an infinite support show up in contemporary cosmology. This paper focuses on the context of inflation theory, where it complicates the assignment of a probability measure over pocket universes. The measure problem in cosmology, whereby it seems impossible to pick out a uniquely well-motivated measure, is associated with a paradox that occurs in standard probability theory and crucially involves uniformity on an infinite sample space. This problem has been discussed by physicists, albeit without reference to earlier work on this topic. The aim of this article is both to introduce philosophers of probability to these recent discussions in cosmology and to familiarize physicists and philosophers working on cosmology with relevant foundational work by Kolmogorov, de Finetti, Jaynes, and other probabilists. As such, the main goal is not to solve the measure problem, but to clarify the exact origin of some of the current obstacles. The analysis of the assumptions going into the paradox indicates that there exist multiple ways of dealing consistently with uniform probabilities on infinite sample spaces. Taking a pluralist stance towards the mathematical methods used in cosmology shows there is some room for progress with assigning probabilities in cosmological theories.

Quantifying the uncertainty of partitions for infinite mixture models

Bayesian clustering models, such as Dirichlet process mixture models (DPMMs), are sophisticated flexible models. They induce a posterior distribution on the set of all partitions of a set of observations. Analysing this posterior distribution is of great interest, but it comes with several challenges. First of all, the number of partitions is overwhelmingly large even for moderate values of the number of observations. Consequently the sample space of the posterior distribution of the partitions is not explored well by MCMC samplers. Second, due to the complexity of representing the uncertainty of partitions, usually only maximum a posteriori estimates of the posterior distribution of partitions are provided and discussed in the literature. In this paper we propose a numerical and graphical method for quantifying the uncertainty of the clusters of a given partition of the data and we suggest how this tool can be used to learn about the partition uncertainty.

Equivalence of information production and generalised entropies in complex processes.

Complex systems with strong correlations and fat-tailed distribution functions have been argued to be incompatible with the Boltzmann-Gibbs entropy framework and alternatives, so-called generalised entropies, were proposed and studied. Here we show, that this perceived incompatibility is actually a misconception. For a broad class of processes, Boltzmann entropy -the log multiplicity- remains the valid entropy concept. However, for non-i.i.d. processes, Boltzmann entropy is not of Shannon form, -k∑ipi log pi, but takes the shape of generalised entropies. We derive this result for all processes that can be asymptotically mapped to adjoint representations reversibly where processes are i.i.d. In these representations the information production is given by the Shannon entropy. Over the original sampling space this yields functionals identical to generalised entropies. The problem of constructing adequate context-sensitive entropy functionals therefore can be translated into the much simpler problem of finding adjoint representations. The method provides a comprehensive framework for a statistical physics of strongly correlated systems and complex processes.

A yield curvature model considering axial compression ratio

This study proposed a yield curvature prediction model considering axial compression ratio with exponential function which is improved on the basis of the specification effective yield curvature prediction model. Parametric moment–curvature curves approach was used to verify that the yield curvature is greatly affected by the section size, the yield strength of longitudinal steel bar and the axial compression ratio. On the basis, the yield curvature under different levels was obtained by Xtract and parametric moment–curvature curves approach, the result shows that with the increase of axial compression ratio, the yield curvature also increases, which is roughly a linear relationship. Subsequently, combined with the specification and considering the influence of axial compression ratio, a new yield curvature prediction model is proposed based on a massive sample space obtained by parametric moment–curvature curves approach. Moreover, by comparing with the experimental database of PEER, the accuracy of new prediction model is verified, and the result shows that the new prediction model is suitable for estimating yield curvature of square section columns.

Hepatic function and structure in feral pigeons (Columba livia domestica) exposed to zinc oxide nanoparticles

BackgroundBroad applications of nanoparticles have invoked the concerns about their impacts on living organisms. This study aimed to evaluate the possible changes that could take place in the liver of feral pigeons (Columba livia domestica) after oral ingestion of zinc oxide nanoparticles (nano-ZnO).MethodsPigeons were acclimatized to the laboratory conditions with a photoperiod of 12:12 h at 20 ± 2 ˚C for 14 days and maintained on seed mixture and clean tap water ad libitum. The birds were randomly assigned to five groups of 10, including one control group and four experimental groups orally receiving 0, 30, 50, and 75 mg/kg b.w. of nano-ZnO through oral gavage for 7 and 14 consecutive days.ResultsThe oxidative stress (OS) biomarkers, namely lipid peroxidation content and catalase activity in the liver samples, and the level of hepatic necrosis markers (ALT, AST, and ALP) in the blood sera were increased in a concentration-dependent manner. Meanwhile, the total antioxidant capacity of liver samples measured by FRAP test was reduced. Histopathological changes revealed inflammatory cell infiltration, swelling area, vacuolization, and expansion of interstitial spaces in liver samples exposed to 75 mg/kg nano-ZnO.ConclusionsNano-ZnO induced obvious hepatotoxicity in the liver of pigeons, where the OS pathway may be the potential mechanism underlying this toxicity.

Proposal of Torque-Correct-by-Construction Optimization Method for Axial Flux Machines

We propose an innovative optimization method for axial flux machine that keeps the output torque constant in the process of weight reduction. A concept of framework is introduced that provides guidance for optimization design on how to choose and redesign the relevant parameters. Unlike the conventional algorithms that search candidates in sample space, new designs with reduced weight are directly determined by following the developed optimization strategy using two-dimensional finite element analysis (FEA). The torque error is restricted by a maximum allowable error introduced in the framework. A single-sided axial flux permanent magnet synchronous machine is used as a starting design. A total of 29 new designs are derived with gradually varying optimization variables. The optimization trend and limit are investigated with respect to the redesigned parameters as well as the resulting weights, which provide an overview for selecting optimal design. At last, the torque of every design is verified by both two-dimensional and three-dimensional FEA. The effectiveness of proposed method is demonstrated by the result that the weight is significantly and continuously reduced as the independent optimization variable increases while the torque error remains in a small extent.

RRT path planning algorithm with enhanced sampling

Due to the low efficiency and low path quality of the RRT algorithm, this paper introduces a sampling-enhanced RRT(SE-RRT) algorithm with four improvements to the RRT algorithm. In the path search phase, the gravitational coefficient is added to improve the efficiency of finding the feasible path without losing randomness. In the path optimization phase, turning points are selected from nodes of the original path by global rewiring. Then the sampling space is established around the turning points for local re-optimization. Finally, a near-optimal path is obtained and can be iterated to the optimal path according to the demand. In the code implementation phase, efficient path collision detection is added to prevent local paths from crossing obstacles.