Local Fitting Research Articles

A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems

A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems

Improvement of normal estimation for point clouds via simplifying surface fitting

During the last decade, stimulated by the roaring success of neural networks in the realm of point clouds, learning-based normal estimation has been a dominating direction and it has attained outstanding performance. The introduction of modern neural networks significantly enhances the robustness of algorithms to noise levels and sampling scales. A method that combines least-squares surface fitting with weight-learning neural networks is particularly prominent. Despite persistent efforts, there is room for improvement. We observed that a simplified surface-fitting process can significantly improve the accuracy of this type of hybrid approach. In this study, two simple yet effective strategies are proposed to boost the accuracy of normal estimation. Specifically, a dynamic Top-K selection strategy is presented to focus on the critical points of a given patch. The selected point hammer fits a local surface using a simple tangent plane, which improves the normal estimation accuracy when encountering patches with sharp corners or complex patterns. Moreover, to further improve the quality of the estimation, a point-update strategy was delicately designed to smooth the sharp boundaries of the patches before the local surface fitting process. Thus, our method can reduce the fitting distortion while simplifying the surface-fitting process. Extensive experiments and ablation studies demonstrated the superiority of our proposed method over contemporary state-of-the-art approaches.

An efficient model for capturing gas transients in the energy flow of integrated electricity–gas systems

Gas-fired units provide great flexibility to accommodate the intermittent nature of renewable energy sources and are also considered to be an ideal substitute for retiring coal-fired generations. However, the increasing deployment of gas-fired units deepens interdependency between electric power and natural gas, calling for efficient modeling of the heterogeneous energy flow in the integrated electricity–gas systems (IEGS). Plenty of research works show the necessity of considering gas flow transients in the IEGS, which can be described by a set of partial differential equations (PDEs). To ensure a tractable optimization, the PDEs are often discretized using the finite/infinite difference method, which may result in a large computational scale for achieving good precision. To this end, this paper proposes an efficient model for capturing gas flow transients in the IEGS, which can be taken as the operational constraints in the optimal energy flow model for IEGS coordinated planning, scheduling, and control. For improving the computational efficiency, the PDE-based transient gas flow model is discretized using a space–time orthogonal collocation (OC) method, which provides high-quality results using fewer spatial–temporal discrete points than differential methods. Further, aiming at the difficulty in the determination of the number of space–time collocation points, an adaptive strategy is proposed and embedded in the proposed model utilizing the local fitting errors of state variables related to pipelines. Case studies show the superiority in computational accuracy and efficiency of the proposed model over the existing models based on Wendorff difference and global OC. And an illustrative case based on the modified IEGS benchmark system composed of the IEEE 118-bus power system and the Belgian 20-node gas network exhibits the application value of the proposed model in the optimization and control of the IEGS.

Optimum adaptive bandwidth selection method of local fitting in kernel regression analysis for non-uniform data

Optimum adaptive bandwidth selection method of local fitting in kernel regression analysis for non-uniform data

RESISTING FRANCHISE CORPORATE CONTROL: HOW THE INTERNET ENABLES LOCAL PRACTICES IN POKÉMON PLAY

Despite being a friendly face for children and an ambassador brand for Japan, Pokémon has always had a strong corporate side. As the Internet and technologies for hacking and communication became more available, players became more disillusioned about the friendliness of Pokémon as a franchise. The controlling nature of Game Freak along with the growing perception of lack of respect for its consumers has created a negative perception of the corporate aspect of the franchise. As a result, a global duality emerged; a corporate, franchise centered component versus a fan based, community-driven culture. In the global nature of Pokémon, where then does the local fit in? How do local Canadian players perceive, negotiate, and resist the corporate and global cultures to make their own play practices? This study examines ways in which the global aspect of Pokémon, both the corporate side and the community culture, influence the play practices of local Canadian players. Through one-on-one interviews, adult Pokémon players who have played Pokémon since the 1990s, provide insights into ways the Internet and other communication technologies have impacted the way they perceive and play Pokémon games. By grounding the nebulous idea of metaplay with 3 components, being metagaming, paratexts, and gaming capital, we can better understand contemporary digital play practices by integrating the communicative nature of the tools players use not only to play their games and expand their experience and knowledge, and how this works not only on a global, cultural level, but also the local, individual level.

Environmental selection, rather than neutral processes, best explain regional patterns of diversity in a tropical rainforest fish

To conserve the high functional and genetic variation in hotspots such as tropical rainforests, it is essential to understand the forces driving and maintaining biodiversity. We asked to what extent environmental gradients and terrain structure affect morphological and genomic variation across the wet tropical distribution of an Australian rainbowfish, Melanotaenia splendida splendida. We used an integrative riverscape genomics and morphometrics framework to assess the influence of these factors on both putative adaptive and non-adaptive spatial divergence. We found that neutral genetic population structure was largely explainable by restricted gene flow among drainages. However, environmental associations revealed that ecological variables had a similar power to explain overall genetic variation, and greater power to explain body shape variation, than the included neutral covariables. Hydrological and thermal variables were the strongest environmental predictors and were correlated with traits previously linked to heritable habitat-associated dimorphism in rainbowfishes. In addition, climate-associated genetic variation was significantly associated with morphology, supporting heritability of shape variation. These results support the inference of evolved functional differences among localities, and the importance of hydroclimate in early stages of diversification. We expect that substantial evolutionary responses will be required in tropical rainforest endemics to mitigate local fitness losses due to changing climates.

Accurate demarcation of a biased nucleus from H&E-stained follicular lymphoma tissues samples

ABSTRACT In recent days, automated demarcation of biased nuclei of follicular lymphoma has become a standard framework in the pipeline of quantitative histopathology. It has received substantial consideration due to subjective variability between different oncologists. This difference can occasionally result in erroneous conclusions, distinct prognostic reports, and inconsistent treatment. This paper provides additional input to an oncologist to ease the prognosis process. This approach defines a local criterion fitting function in neighbourhood of each point based on image intensity. An assumption is pixel intensity will remain constant next to the point. Integration of these local neighbourhood centres leads us to define the global criterion of image segmentation. Segmentation accuracy is evaluated using region-based measures and the optimal segmentation accuracy of 98.6% is achieved using the DICE coefficient. By using locally formulated energy, execution time is also reduced in comparison to the contour algorithm. This technique is independent of the initialization.

Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.

Robust local quantile regression for reference curves

We propose two non-parametric methods to construct locally fitted quantile reference curves that are robust with respect to outliers in the predictor variable. The first includes a weighting procedure and the second, the detection and subsequent elimination of outlying predictor variable values before the local fitting process. The reference curves fitted by the proposed methods generate quantile limits that are less affected in regions with a low frequency of the predictor variable values. The proposed procedures are used to fit reference curves to data extracted from a study conducted at the Heart Institute of the University of São Paulo Medical School.

A hybrid mixed finite element method for convection-diffusion-reaction equation with local exponential fitting technique

A new hybrid mixed finite element method is proposed for solving the convection-diffusion-reaction equation with local exponential fitting technique. In this method, the exponential fitting technique in [7] is used to discretize the convection-diffusion-reaction equation by introducing a new variable at element level; then the hybrid mixed discontinuous Galerkin finite element method is used to approximate the discretized problem. The convergence of the proposed method is analyzed, and the corresponding a priori error estimate is derived. The numerical results are presented to confirm our theoretical analysis.

Living well: Empirically developed structural equation model for healthy and effective self-regulation

The purpose of this study is to develop and empirically test a structural equation model (SEM) for healthy and effective self-regulation based on the propositions of self-determination theory (SDT). A cross-sectional data sample (N = 6,705) is used to test the model. The results of the SEM demonstrate good to excellent global fit indices (RMSEA = 0.06, SRMR = 0.04 CFI = 0.97, TLI/NNFI = 0.95) and excellent local fit indices (p < 0.001). It is acknowledged that longitudinal and experimental research designs will be necessary to infer causal effects. However, based on the strong theoretical and empirical grounding of the model, indications for causal effects are discussed beyond correlational relations. The local fit indices imply that autonomy of goals, intrinsic values orientation, mindfulness, and the newly integrated construct clarity about personal values positively affect psychological needs satisfaction and facets of subjective and psychological well-being. Additionally, they indicate that mindfulness and clarity about personal values have the greatest benefits on individual health, well-being, and effectiveness. These results are crucial as they emphasize the significant role of mindfulness in healthy and effective self-regulation. Furthermore, they put the spotlight on a rather new construct; clarity about personal values. By having transferred the knowledge base of SDT into an empirically derived model of healthy and effective self-regulation, this study provides well-grounded indications of how health, well-being, and effectiveness in individuals may be fostered. These indications offer new insights for theory building and practical interventions in domains like psychotherapy, healthcare, organizations, sports, and education.

A local Gaussian Processes method for fitting potential surfaces that obviates the need to invert large matrices

In order to compute a vibrational spectrum, one often wishes to start with a set of ab initio Born–Oppenheimer potential values at points, called fitting points, and interpolate or fit to find values of the potential at quadrature or collocation points. It is common to do this once to build a potential energy surface (PES). Once the PES is known, it can be evaluated at any point in configuration space. Gaussian Process (GP) is frequently being used to make a PES. As is the case in other interpolation methods, to use GP one must store and invert a matrix whose size is the number of fitting points. The matrix is sometimes large enough that approximations are introduced to reduce the cost of the calculation. We show that is possible to use many local Gaussian Process fits rather than one global fit. Retaining only local Gaussians and the associated points works well despite the fact that other Gaussians have tails with significant amplitude in the local region. We demonstrate that from the potential values obtained from the local fits it is possible to compute accurate energy levels of formaldehyde. In one calculation, potential values were obtained with N=120,000 fitting points by inverting matrices of size less than m=400. The local idea reduces the cost from N3 to T(m3+N), where T is the number of desired potential points.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

The multitude of artificial intelligence (AI)–based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Magic, metaphysics, and methods

Magic, metaphysics, and methods

Niche theory for positive plant-soil feedbacks.

Interactions between plants and the soil are an important ecological process in terrestrial ecosystems as they affect plant community structure: when and where we find different plant species. Those interactions are typically thought of as one-directional: local soil conditions filter through dispersing species to produce a community of locally adapted plants. However, plants can modify local physicochemical soil conditions via their roots and associations with soil microbes. These may in turn affect the local fitness of other plants, making plant-soil interactions bidirectional. In order to understand how they differ from other ecological processes that structure plant communities, we need a theory connecting these individual-level plant-soil feedbacks to community-level patterns. Here, we build this theory with a mathematical model of plant community dynamics in which soil conditioning is explicitly modeled over time and depends on the density of the plants. We analyze this model to describe the long-term composition and spatial distribution of the plant community. Our main result is that positive plant-soil feedbacks will create clustering of species with similar soil preferences. The composition of these clusters is further influenced by niche width and conditioning strength. In contrast with competitive dynamics driven by niche overlap, only species belonging to the same cluster can maintain high relative abundance in the community. Spatial heterogeneity in the form of an environmental gradient generates patches, each representing a single cluster. However, such patchiness is disfavored when species differ in dispersal ability. We show that stronger dispersers cannot take over the habitat as long as an exogenous driver favors soil conditions that benefit the other species. If exogenous drivers supersede soil conditioning by plants, we retrieve classic habitat filtering, where species are selected based on their suitability to the local environment. Overall, we provide a novel mathematical model for positive plant-soil feedback that we use to describe the spatial patterns of plant abundance and traits related to soil preference and conditioning ability.

Perceived Body Image towards Disordered Eating Behaviors and Supplement Use: A Study of Mauritian Gym-Goers

Purpose: Individuals with body image distortions tend to develop an obsession with calorie intake and weight, leading to compounded coping strategies such as modified eating behaviors, supplement use, and excessive exercise. In the local context, little is known about the equation connecting body image to improper eating behaviors and other potential coping mechanisms. This study primarily aims at critically identifying the nexus between features of disordered eating behavior including supplement use, and body image. Methodology: A probabilistic randomized sample of 305 gym members was assessed based on their level of body satisfaction and its influence on their eating habits and supplement use through a structured survey. Findings: Only 37% of the participants were satisfied with their body image. The results concurred with the notion of a poor body image influencing body esteem (χ2 (10) = 137.21, p &lt; 0.05; Cramer V = 0.474). While exercise motivated the use of supplements, a significant relationship was also observed between disordered eating habits and supplement use (χ2 (4) = 48.63, p &lt; 0.05; Cramer V = 0.399). A predictive model was successfully drawn inclusive of the disordered eating behaviors dimensions as a potent predictor of body image (χ2 (56) = 397.12, p &lt; 0.001. Implications: This study presents an in-depth analysis of the magnitude of disordered eating behaviors with respect to individuals in the local fitness industry, and the need for professional support programs toward the re-alignment of body image in an effort to curtail the development of eating disorders in Mauritian gym-goers.

A Parallel Multiobjective PSO Weighted Average Clustering Algorithm Based on Apache Spark

Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes it difficult for existing algorithms to handle large-scale data. With the development of distributed parallel computing framework, data parallelism was proposed. However, the increase in parallelism will lead to the problem of unbalanced data distribution affecting the clustering effect. In this paper, we propose a parallel multiobjective PSO weighted average clustering algorithm based on apache Spark (Spark-MOPSO-Avg). First, the entire data set is divided into multiple partitions and cached in memory using the distributed parallel and memory-based computing of Apache Spark. The local fitness value of the particle is calculated in parallel according to the data in the partition. After the calculation is completed, only particle information is transmitted, and there is no need to transmit a large number of data objects between each node, reducing the communication of data in the network and thus effectively reducing the algorithm's running time. Second, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results. Experimental results show that the Spark-MOPSO-Avg algorithm achieves lower information loss under data parallelism, losing about 1% to 9% accuracy, but can effectively reduce the algorithm time overhead. It shows good execution efficiency and parallel computing capability under the Spark distributed cluster.

Segmentation of Nucleus and Cytoplasm from H&amp;E-Stained Follicular Lymphoma

This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&amp;E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.

Analytic gradients for local density fitting Hartree-Fock and Kohn-Sham methods.

We present analytic gradients for local density fitting Hartree-Fock (HF) and hybrid Kohn-Sham (KS) density functional methods. Due to the non-variational nature of the local fitting algorithm, the method of Lagrange multipliers is used to avoid the solution of the coupled perturbed HF and KS equations. We propose efficient algorithms for the solution of the arising Z-vector equations and the gradient calculation that preserve the third-order scaling and low memory requirement of the original local fitting algorithm. In order to demonstrate the speed and accuracy of our implementation, gradient calculations and geometry optimizations are presented for various molecular systems. Our results show that significant speedups can be achieved compared to conventional density fitting calculations without sacrificing accuracy.

A local fitting based multifractal detrend fluctuation analysis method

In this study, we propose a one-dimensional multifractal local fitting detrending fluctuation analysis (MF-LF-DFA) algorithm using traditional multifractal detrended fluctuation analysis (MF-DFA). The classical MF-DFA uses the least squares technique to fit the local trend of the time series, and the fitting order is fixed. However, the proposed MF-LF-DFA algorithm sets the fitting order reasonably for different local intervals according to the fluctuation characteristics of the sequence. We examine the performances of MF-DFA and MF-LF-DFA using the time series made using p-model multiplication cascade. By comparing the numerical results and analytical values of the generalized Hurst exponent H(q), the Scaling exponent τ(q) and multifractal spectrum f(α), the empirical tests demonstrate that H(q), τ(q) and f(α) calculated by MF-LF-DFA algorithm are closer to the analytical values. Furthermore, we compute the relative errors Herr and τerr of the two methods, and the relative errors calculated by MF-LF-DFA are smaller. Besides, we examine the monofractal structure of MF-LF-DFA by constructing a random sequence and calculating the value of H(2). In addition, we generate and test shuffled and phase Fourier randomized sequences of the original random time series and multiplicative cascade time series. We also study different length sequences within the same model, and verify the stability of the results. Finally, we change the range of segmentation window s and the fitting order of MF-DFA model to check the robust property of the proposed MF-LF-DFA algorithm. The computational results indicate that MF-LF-DFA outperforms the traditional MF-DFA in all situations.