Статті в журналах з теми "Globally optimal inference"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Globally optimal inference.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-23 статей у журналах для дослідження на тему "Globally optimal inference".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Clarke, J., and M. Lapata. "Global Inference for Sentence Compression: An Integer Linear Programming Approach." Journal of Artificial Intelligence Research 31 (March 11, 2008): 399–429. http://dx.doi.org/10.1613/jair.2433.
Повний текст джерелаАнотація:
Sentence compression holds promise for many applications ranging from summarization to subtitle generation. Our work views sentence compression as an optimization problem and uses integer linear programming (ILP) to infer globally optimal compressions in the presence of linguistically motivated constraints. We show how previous formulations of sentence compression can be recast as ILPs and extend these models with novel global constraints. Experimental results on written and spoken texts demonstrate improvements over state-of-the-art models.
2
Chan, Schultz, and Malay Ghosh. "The Geometry of Estimating Functions in the Presence of Nuisance Parameters 1." Calcutta Statistical Association Bulletin 48, no. 3-4 (September 1998): 123–38. http://dx.doi.org/10.1177/0008068319980301.
Повний текст джерелаАнотація:
The paper studies the geometry of estimating functions in the presence of nuisance parameters. The basic technique involves an idea of orthogonal projection first introduced by Small and McLeish (1989, 1991, 1992, 1994) in this context. The three main topics are : (A) globally optimal estimating functons; (B) locally optimal estimating functions; (C) conditionally optimal estimating functions. A general result is derived in each case. As special cases, we extend and unify some of the results already available in the literature. In particular, as special cases of our result on globally optimal estimating functions, we find the results of Godambe and Thompson (1974) and Godambe (1976) with nuisance parameters. We provide also a geometric interpretation of conditional and marginal inference of Bhapkar (1989, 1991) and Lloyd (1987) . As application of our result on locally optimal estimating functions, Godambe's (1985) result on optimal estimating functions for stochastic processes is extended to nuisance parameters. Finally, our general result on conditionally optimal estimating function helps to generalize the findings of Godambe and Thompson (1989) to situations which admit the presence of nuisance parameters.
3
Li, Xingjie Helen, Fei Lu, and Felix X. F. Ye. "ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems." Discrete & Continuous Dynamical Systems - S 15, no. 4 (2022): 747. http://dx.doi.org/10.3934/dcdss.2021103.
Повний текст джерелаАнотація:
<p style='text-indent:20px;'>Efficient simulation of SDEs is essential in many applications, particularly for ergodic systems that demand efficient simulation of both short-time dynamics and large-time statistics. However, locally Lipschitz SDEs often require special treatments such as implicit schemes with small time-steps to accurately simulate the ergodic measures. We introduce a framework to construct inference-based schemes adaptive to large time-steps (ISALT) from data, achieving a reduction in time by several orders of magnitudes. The key is the statistical learning of an approximation to the infinite-dimensional discrete-time flow map. We explore the use of numerical schemes (such as the Euler-Maruyama, the hybrid RK4, and an implicit scheme) to derive informed basis functions, leading to a parameter inference problem. We introduce a scalable algorithm to estimate the parameters by least squares, and we prove the convergence of the estimators as data size increases.</p><p style='text-indent:20px;'>We test the ISALT on three non-globally Lipschitz SDEs: the 1D double-well potential, a 2D multiscale gradient system, and the 3D stochastic Lorenz equation with a degenerate noise. Numerical results show that ISALT can tolerate time-step magnitudes larger than plain numerical schemes. It reaches optimal accuracy in reproducing the invariant measure when the time-step is medium-large.</p>
4
Rosen, David M., Luca Carlone, Afonso S. Bandeira, and John J. Leonard. "SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group." International Journal of Robotics Research 38, no. 2-3 (August 29, 2018): 95–125. http://dx.doi.org/10.1177/0278364918784361.
Повний текст джерелаАнотація:
Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown group elements [Formula: see text] given noisy measurements of a subset of their pairwise relative transforms [Formula: see text]. Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a non-convex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation (MLE) whose minimizer provides an exact maximum-likelihood estimate so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so significantly faster than the Gauss–Newton-based approach that forms the basis of current state-of-the-art techniques.
5
Inyang, Udoinyang Godwin, Emem Etok Akpan, and Oluwole Charles Akinyokun. "A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification." International Journal of Computational Intelligence and Applications 19, no. 02 (June 2020): 2050012. http://dx.doi.org/10.1142/s1469026820500121.
Повний текст джерелаАнотація:
Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset. [Formula: see text]-means based on silhouette criterion discovered top three representatives of the optimal numbers of clusters inherent in the flood dataset. Experts’ judgment favored four clusters, while Squared Euclidean distance was the best performing distance measure. SOM provided cluster visuals of the input attributes within the four different groups and transformed the dataset into a labeled one. A 5-layered Adaptive Neuro Fuzzy Inference System (ANFIS) driven by hybrid learning algorithm was applied to classify and predict FSL. ANFIS optimized by Genetic Algorithm (GA) produced root mean squared error (RMSE) of 0.323 and Error Standard Deviation of 0.408 while Particle Swarm Optimized ANFIS model produced 0.288 as the RMSE, depicting 11% improvement when compared with GA optimized model. The result shows significant improvement in the classification and prediction of flood risks using single ML tool.
6
Huang, Shu-Chiang, Shui-Kai Chang, Chi-Chang Lai, Tzu-Lun Yuan, Jinn-Shing Weng, and Jia-Sin He. "Length–Weight Relationships, Growth Models of Two Croakers (Pennahia macrocephalus and Atrobucca nibe) off Taiwan and Growth Performance Indices of Related Species." Fishes 7, no. 5 (October 11, 2022): 281. http://dx.doi.org/10.3390/fishes7050281.
Повний текст джерелаАнотація:
Information on age and growth is essential to modern stock assessment and the development of management plans for fish resources. To provide quality otolith-based estimates of growth parameters, this study performed five types of analyses on the two important croakers that were under high fishing pressure in southwestern Taiwan: Pennahia macrocephalus (big-head pennah croaker) and Atrobucca nibe (blackmouth croaker): (1) Estimation of length–weight relationships (LWR) with discussion on the differences with previous studies; (2) validation of the periodicity of ring formation using edge analysis; (3) examination of three age determination methods (integral, quartile and back-calculation methods) and selection of the most appropriate one using a k-fold cross-validation simulation; (4) determination of the representative growth models from four candidate models using a multimodel inference approach; and, (5) compilation of growth parameters for all Pennahia and Atrobucca species published globally for reviewing the clusters of estimates using auximetric plots of logged growth parameters. The study observed that features of samples affected the LWR estimates. Edge analysis supported the growth rings were formed annually, and the cross-validation study supported the quartile method (age was determined as the number of opaque bands on otolith plus the quartile of the width of the marginal translucent band) provided more appropriate estimates of age. The multimodel inference approach suggested the von Bertalanffy growth model as the optimal model for P. macrocephalus and logistic growth model for A. nibe, with asymptotic lengths and relative growth rates of 18.0 cm TL and 0.789 year−1 and 55.21 cm, 0.374 year−1, respectively. Auximetric plots of global estimates showed a downward trend with clusters by species. Growth rates of the two species were higher than in previous studies using the same aging structure (otolith) and from similar locations conducted a decade ago, suggesting a possible effect of increased fishing pressure and the need to establish a management framework. This study adds updated information to the global literature and provides an overview of growth parameters for the two important croakers.
7
Tian, Luogeng, Bailong Yang, Xinli Yin, Kai Kang, and Jing Wu. "Multipath Cross Graph Convolution for Knowledge Representation Learning." Computational Intelligence and Neuroscience 2021 (December 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/2547905.
Повний текст джерелаАнотація:
In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.
8
Bauer-Marschallinger, Bernhard, Senmao Cao, Mark Edwin Tupas, Florian Roth, Claudio Navacchi, Thomas Melzer, Vahid Freeman, and Wolfgang Wagner. "Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube." Remote Sensing 14, no. 15 (July 31, 2022): 3673. http://dx.doi.org/10.3390/rs14153673.
Повний текст джерелаАнотація:
Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS).
9
Hou, Jiawei, Albert I. J. M. van Dijk, Hylke E. Beck, Luigi J. Renzullo, and Yoshihide Wada. "Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale." Hydrology and Earth System Sciences 26, no. 14 (July 19, 2022): 3785–803. http://dx.doi.org/10.5194/hess-26-3785-2022.
Повний текст джерелаАнотація:
Abstract. Many thousands of large dam reservoirs have been constructed worldwide during the last 70 years to increase reliable water supplies and support economic growth. Because reservoir storage measurements are generally not publicly available, so far there has been no global assessment of long-term dynamic changes in reservoir water volumes. We overcame this by using optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6695 reservoirs worldwide between 1984 and 2015. We relate reservoir storage to resilience and vulnerability and investigate interactions between precipitation, streamflow, evaporation, and reservoir water storage. This is based on a comprehensive analysis of streamflow from a multi-model ensemble and as observed at ca. 8000 gauging stations, precipitation from a combination of station, satellite and forecast data, and open water evaporation estimates. We find reservoir storage has diminished substantially for 23 % of reservoirs over the three decades, but increased for 21 %. The greatest declines were for dry basins in southeastern Australia (−29 %), southwestern USA (−10 %), and eastern Brazil (−9 %). The greatest gains occurred in the Nile Basin (+67 %), Mediterranean basins (+31 %) and southern Africa (+22 %). Many of the observed reservoir changes could be explained by changes in precipitation and river inflows, emphasizing the importance of multi-decadal precipitation changes for reservoir water storage. Uncertainty in the analysis can come from, among others, the relatively low Landsat imaging frequency for parts of the Earth and the simple geo-statistical bathymetry model used. Our results also show that there is generally little impact from changes in net evaporation on storage trends. Based on the reservoir water balance, we deduce it is unlikely that water release trends dominate global trends in reservoir storage dynamics. This inference is further supported by different spatial patterns in water withdrawal and storage trends globally. A more definitive conclusion about the impact of changes in water releases at the global or local scale would require data that unfortunately are not publicly available for the vast majority of reservoirs globally.
10
Sonali, P., and D. Nagesh Kumar. "Review of recent advances in climate change detection and attribution studies: a large-scale hydroclimatological perspective." Journal of Water and Climate Change 11, no. 1 (February 5, 2020): 1–29. http://dx.doi.org/10.2166/wcc.2020.091.
Повний текст джерелаАнотація:
Abstract The rapid changes in global average surface temperature have unfathomed influences on human society, environment, ecosystem, availability of food and fresh water. Multiple lines of evidence indicate that warming of the climate system is unequivocal, and human-induced effects are playing an enhanced role in climate change. It is of utmost importance to ascertain the hydroclimatological changes in order to ascertain the characteristics of detection and attribution (D&A) of human-induced anthropogenic influences on recent warming. Climate change D&A are interrelated. Their study enhances our understanding about the rudimentary causes leading to climate changes and hence, considered as a decisive element in all Intergovernmental Panel on Climate Change Assessment Reports. An extensive discussion of the concerned scientific literature on climate change D&A is indispensably needed for the scientific community to assess climate change threats in clear terms. This study has reviewed various processes and advances in climate change D&A analyses at global/regional scales during the past few decades. Regression-based optimal fingerprint approach is majorly employed in climate change D&A studies. The accumulation of inferences presented in this study from numerous studies could be extremely helpful for the scientific community and policymakers as they deal with climate change adaptation and mitigation challenges.
11
Snider, Graydon, Crystal L. Weagle, Kalaivani K. Murdymootoo, Amanda Ring, Yvonne Ritchie, Emily Stone, Ainsley Walsh, et al. "Variation in global chemical composition of PM<sub>2.5</sub>: emerging results from SPARTAN." Atmospheric Chemistry and Physics 16, no. 15 (August 2, 2016): 9629–53. http://dx.doi.org/10.5194/acp-16-9629-2016.
Повний текст джерелаАнотація:
Abstract. The Surface PARTiculate mAtter Network (SPARTAN) is a long-term project that includes characterization of chemical and physical attributes of aerosols from filter samples collected worldwide. This paper discusses the ongoing efforts of SPARTAN to define and quantify major ions and trace metals found in fine particulate matter (PM2.5). Our methods infer the spatial and temporal variability of PM2.5 in a cost-effective manner. Gravimetrically weighed filters represent multi-day averages of PM2.5, with a collocated nephelometer sampling air continuously. SPARTAN instruments are paired with AErosol RObotic NETwork (AERONET) sun photometers to better understand the relationship between ground-level PM2.5 and columnar aerosol optical depth (AOD).We have examined the chemical composition of PM2.5 at 12 globally dispersed, densely populated urban locations and a site at Mammoth Cave (US) National Park used as a background comparison. So far, each SPARTAN location has been active between the years 2013 and 2016 over periods of 2–26 months, with an average period of 12 months per site. These sites have collectively gathered over 10 years of quality aerosol data. The major PM2.5 constituents across all sites (relative contribution ± SD) are ammoniated sulfate (20 % ± 11 %), crustal material (13.4 % ± 9.9 %), equivalent black carbon (11.9 % ± 8.4 %), ammonium nitrate (4.7 % ± 3.0 %), sea salt (2.3 % ± 1.6 %), trace element oxides (1.0 % ± 1.1 %), water (7.2 % ± 3.3 %) at 35 % RH, and residual matter (40 % ± 24 %).Analysis of filter samples reveals that several PM2.5 chemical components varied by more than an order of magnitude between sites. Ammoniated sulfate ranges from 1.1 µg m−3 (Buenos Aires, Argentina) to 17 µg m−3 (Kanpur, India in the dry season). Ammonium nitrate ranged from 0.2 µg m−3 (Mammoth Cave, in summer) to 6.8 µg m−3 (Kanpur, dry season). Equivalent black carbon ranged from 0.7 µg m−3 (Mammoth Cave) to over 8 µg m−3 (Dhaka, Bangladesh and Kanpur, India). Comparison of SPARTAN vs. coincident measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network at Mammoth Cave yielded a high degree of consistency for daily PM2.5 (r2 = 0.76, slope = 1.12), daily sulfate (r2 = 0.86, slope = 1.03), and mean fractions of all major PM2.5 components (within 6 %). Major ions generally agree well with previous studies at the same urban locations (e.g. sulfate fractions agree within 4 % for 8 out of 11 collocation comparisons). Enhanced anthropogenic dust fractions in large urban areas (e.g. Singapore, Kanpur, Hanoi, and Dhaka) are apparent from high Zn : Al ratios.The expected water contribution to aerosols is calculated via the hygroscopicity parameter κv for each filter. Mean aggregate values ranged from 0.15 (Ilorin) to 0.28 (Rehovot). The all-site parameter mean is 0.20 ± 0.04. Chemical composition and water retention in each filter measurement allows inference of hourly PM2.5 at 35 % relative humidity by merging with nephelometer measurements. These hourly PM2.5 estimates compare favourably with a beta attenuation monitor (MetOne) at the nearby US embassy in Beijing, with a coefficient of variation r2 = 0.67 (n = 3167), compared to r2 = 0.62 when κv was not considered. SPARTAN continues to provide an open-access database of PM2.5 compositional filter information and hourly mass collected from a global federation of instruments.
12
Shi, Ming, Sheng Tan, Xin-Ping Xie, Ao Li, Wulin Yang, Tao Zhu, and Hong-Qiang Wang. "Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data." BMC Genomics 21, no. 1 (October 14, 2020). http://dx.doi.org/10.1186/s12864-020-07079-8.
Повний текст джерелаАнотація:
Abstract Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.
13
Qu, Zhongjun, Jungmo Yoon, and Pierre Perron. "Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits." Review of Economics and Statistics, January 25, 2022, 1–45. http://dx.doi.org/10.1162/rest_a_01168.
Повний текст джерелаАнотація:
Abstract We propose methods to estimate and conduct inference on conditional quantile processes for models with both nonparametric and (locally or globally) linear components. We derive their asymptotic properties, optimal bandwidths, and uniform confidence bands over quantiles allowing for robust bias correction. Our framework covers the sharp regression discontinuity design, which is used to study the effects of unemployment insurance benefits extensions, focusing on heterogeneity over quantiles and covariates. We show economically strong effects in the tails of the outcome distribution. They reduce the within-group inequality, but can be viewed as enhancing between-group inequality, although helping to bridge the gender gap.
14
Islam, Md Saiful, Md Sarowar Morshed, and Md Noor-E-Alam. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference." INFORMS Journal on Computing, August 10, 2022. http://dx.doi.org/10.1287/ijoc.2022.1226.
Повний текст джерелаАнотація:
Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models have been proposed to tackle such uncertainty; however, they produce 0-1 nonlinear problems and lack scalability. In this work, we investigate this emerging data science problem and develop a unique computational framework to solve the robust causal inference test instances from observational data with continuous outcomes. In the proposed framework, we first reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve a globally optimal solution. We perform experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms and compare our results with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process. Finally, the proposed framework not only facilitates robust decision making through big-data causal inference, but it can also be utilized in developing efficient algorithms for other nonlinear optimization problems such as quadratic assignment problems.
15
Wu, Jie, Yangxiu Liu, and Yiqiang Zhao. "Systematic Review on Local Ancestor Inference From a Mathematical and Algorithmic Perspective." Frontiers in Genetics 12 (May 24, 2021). http://dx.doi.org/10.3389/fgene.2021.639877.
Повний текст джерелаАнотація:
Genotypic data provide deep insights into the population history and medical genetics. The local ancestry inference (LAI) (also termed local ancestry deconvolution) method uses the hidden Markov model (HMM) to solve the mathematical problem of ancestry reconstruction based on genomic data. HMM is combined with other statistical models and machine learning techniques for particular genetic tasks in a series of computer tools. In this article, we surveyed the mathematical structure, application characteristics, historical development, and benchmark analysis of the LAI method in detail, which will help researchers better understand and further develop LAI methods. Firstly, we extensively explore the mathematical structure of each model and its characteristic applications. Next, we use bibliometrics to show detailed model application fields and list articles to elaborate on the historical development. LAI publications had experienced a peak period during 2006–2016 and had kept on moving in the following years. The efficiency, accuracy, and stability of the existing models were evaluated by the benchmark. We find that phased data had higher accuracy in comparison with unphased data. We summarize these models with their distinct advantages and disadvantages. The Loter model uses dynamic programming to obtain a globally optimal solution with its parameter-free advantage. Aligned bases can be used directly in the Seqmix model if the genotype is hard to call. This research may help model developers to realize current challenges, develop more advanced models, and enable scholars to select appropriate models according to given populations and datasets.
16
Hassanzadeh, Mojtaba, and Zahra Rahmani. "An Intelligent Predictive Controller for Power and Battery Management in Plug-In Hybrid Electric Vehicles." Journal of Energy Resources Technology 143, no. 11 (April 19, 2021). http://dx.doi.org/10.1115/1.4050577.
Повний текст джерелаАнотація:
Abstract This paper presents a novel real-time energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs), which combines the adaptive neuro-fuzzy inference system (ANFIS) and the model predictive control (MPC). A two-objective EMS with two state variables is defined by integrating the battery aging and fuel economy in the objective function. First, the dynamic programming (DP) approach is applied offline to obtain the globally optimal solutions. Then, a real-time predictive EMS is proposed, in which DP carries out a moving-horizon optimization. Contrary to the charge-sustaining HEVs, the optimal trajectory of the battery state-of-charge (SOC) in PHEVs does not fluctuate around a constant level. Thus, determining the desired value of SOC for the real-time moving-horizon optimization is a challenging issue. Unlike the EMSs with a pre-determined reference for SOC, a trained ANFIS model constructs the real-time sub-optimal SOC trajectory in advance. Finally, the effectiveness of the proposed approach is shown through simulation. The proposed EMS is examined over multiple real-time driving cycles, and the results indicate that the total cost is increased compared to those unaware of battery aging. The real-time EMS is then compared to different approaches. While suboptimal, the proposed EMS is real-time implementable, and the results are found to be close enough to those of optimal controller, compared to the two other tested approaches.
17
Chang, Shui-Kai, Tzu-Lun Yuan, Simon D. Hoyle, Jessica H. Farley, and Jen-Chieh Shiao. "Growth Parameters and Spawning Season Estimation of Four Important Flyingfishes in the Kuroshio Current Off Taiwan and Implications From Comparisons With Global Studies." Frontiers in Marine Science 8 (January 4, 2022). http://dx.doi.org/10.3389/fmars.2021.747382.
Повний текст джерелаАнотація:
Growth shapes the life history of fishes. Establishing appropriate aging procedures and selecting representative growth models are important steps in developing stock assessments. Flyingfishes (Exocoetidae) have ecological, economic, and cultural importance to many coastal countries including Taiwan. There are 29 species of flyingfishes found in the Kuroshio Current off Taiwan and adjacent waters, comprising 56% of the flyingfishes taxa recorded worldwide. Among the six dominant species in Taiwan, four are of special importance. This study reviews aging data of these four species, documents major points of the aging methods to address three aging issues identified in the literature, and applies multi-model inference to estimate sex-combined and sex-specific growth parameters for each species. The candidate growth models examined included von Bertalanffy, Gompertz, Logistic, and Richards models, and the resulting optimal model tended to be the von Bertalanffy model for sex-combined data and Gompertz and von Bertalanffy models for sex-specific cases. The study also estimates hatch dates from size data collected from 2008 to 2017; the results suggest that the four flyingfishes have two spawning seasons per year. Length-weight relationships are also estimated for each species. Finally, the study combines the optimal growth estimates from this study with estimates for all flyingfishes published globally, and statistically classifies the estimates into clusters by hierarchical clustering analysis of logged growth parameters. The results demonstrate that aging materials substantially affect growth parameter estimates. This is the first study to estimate growth parameters of flyingfishes with multiple model consideration. This study provides advice for aging flyingfishes based on the three aging issues and the classification analysis, including a recommendation of using the asterisci for aging flyingfishes to avoid complex otolith processing procedures, which could help researchers from coastal countries to obtain accurate growth parameters for many flyingfishes.
18
Wang, Y. Curtis, Johann Rudi, James Velasco, Nirvik Sinha, Gideon Idumah, Randall K. Powers, Charles J. Heckman, and Matthieu K. Chardon. "Multimodal parameter spaces of a complex multi-channel neuron model." Frontiers in Systems Neuroscience 16 (October 20, 2022). http://dx.doi.org/10.3389/fnsys.2022.999531.
Повний текст джерелаАнотація:
One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.
19
Maltamo, Matti, Petteri Packalen, and Annika Kangas. "From comprehensive field inventories to remotely sensed wall-to-wall stand attribute data - a brief history of management inventories in the Nordic countries." Canadian Journal of Forest Research, December 2, 2020. http://dx.doi.org/10.1139/cjfr-2020-0322.
Повний текст джерелаАнотація:
Forest Management Inventories (FMIs) provide critical information, usually at the stand level, for forest management planning. A typical FMI includes i) the delineation of the inventory area to stands by applying auxiliary information, ii) the classification of the stands according to categorical attributes, such as age, site fertility, main tree species, stand development, and iii) measurement, modelling and prediction of stand attributes of interest. The emergence of wall-to-wall remote-sensing data has enabled a paradigm change in FMIs from highly subjective, visual assessments to objective, model-based inferences. Previously, optical remote-sensing data were used to complement visual assessments, especially in stand delineation and height measurements. The evolution of airborne laser scanning (ALS) has made objective estimation of forest characteristics with known accuracy possible. New optical and Lidar-based sensors and platforms will allow further improvements of accuracy. However, there are still bottlenecks related to species-specific stand attribute information in mixed stands and assessments of tree quality. Here we concentrate on approaches and methods that have been applied in the Nordic countries in particular.
20
Bowlby, Heather D., Hugues P. Benoît, Warren Joyce, James Sulikowski, Rui Coelho, Andrés Domingo, Enric Cortés, et al. "Beyond Post-release Mortality: Inferences on Recovery Periods and Natural Mortality From Electronic Tagging Data for Discarded Lamnid Sharks." Frontiers in Marine Science 8 (April 7, 2021). http://dx.doi.org/10.3389/fmars.2021.619190.
Повний текст джерелаАнотація:
Accurately characterizing the biology of a pelagic shark species is critical when assessing its status and resilience to fishing pressure. Natural mortality (M) is well known to be a key parameter determining productivity and resilience, but also one for which estimates are most uncertain. While M can be inferred from life history, validated direct estimates are extremely rare for sharks. Porbeagle (Lamna nasus) and shortfin mako (Isurus oxyrinchus) are presently overfished in the North Atlantic, but there are no directed fisheries and successful live release of bycatch is believed to have increased. Understanding M, post-release mortality (PRM), and variables that affect mortality are necessary for management and effective bycatch mitigation. From 177 deployments of archival satellite tags, we inferred mortality events, characterized physiological recovery periods following release, and applied survival mixture models to assess M and PRM. We also evaluated covariate effects on the duration of any recovery period and PRM to inform mitigation. Although large sample sizes involving extended monitoring periods (>90 days) would be optimal to directly estimate M from survival data, it was possible to constrain estimates and infer probable values for both species. Furthermore, the consistency of M estimates with values derived from longevity information suggests that age determination is relatively accurate for these species. Regarding bycatch mitigation, our analyses suggest that juvenile porbeagle are more susceptible to harm during capture and handling, that keeping lamnid sharks in the water during release is optimal, and that circle hooks are associated with longer recovery periods for shortfin mako.
21
Zhu, Lixin, Changzi Ge, Zhaoyang Jiang, Chunli Liu, Gang Hou, and Zhenlin Liang. "Stock Assessment of Small Yellow Croaker (Larimichthys polyactis) Off the Coast of China Using Per-Recruit Analysis Based on Bayesian Inference." Frontiers in Marine Science 8 (November 30, 2021). http://dx.doi.org/10.3389/fmars.2021.652293.
Повний текст джерелаАнотація:
This paper presents a framework for quantifying uncertainty in per-recruit analysis for small yellow croaker (Larimichthys polyactis) fisheries in China, in which credible estimates of life history parameters from Bayesian inference were used to generate the distribution for a quantity of interest. Small yellow croakers were divided into five spatial groups. The status of each group was examined using a yield-per-recruit (YPR) model and a spawning stock biomass-per-recruit (SSBPR) model. The optimal length at first capture (Lcopt) was proposed to recover the biomass. The maximum observed age in the current stocks (3 years) and the maximum recorded age (≥20 years) were adopted in per-recruit analysis. Our results suggest that the framework can quantify uncertainty well in the output of per-recruit analysis for small yellow croaker. It is suited to other fish species. The SSBPR at FMSY (SSBPRMSY) is a better benchmark than the spawning potential ratio (SPR) at FMSY because SSBPRMSY had a unimodal distribution. The SSBPR analysis can lead to a more conservative Lcopt than the YPR analysis. The key factor influencing the assessment conclusions may be the growth parameters rather than the natural mortality rate for a stock with a younger maximum age. Overfishing likely occurred for all groups and recruitment overfishing may not occur if the maximum age is maintained at 3 years. Increasing lengths at first capture to the recommended values can help this population recover. However, Fcur is too high for small yellow croakers to attain the maximum recorded age. Both reducing fishing mortality rate and increasing length at first capture are needed to attain the maximum recorded age.
22
Ong, Chian Teng, Elizabeth M. Ross, Gry Boe-Hansen, Conny Turni, Ben J. Hayes, Geoffry Fordyce, and Ala E. Tabor. "Adaptive sampling during sequencing reveals the origins of the bovine reproductive tract microbiome across reproductive stages and sexes." Scientific Reports 12, no. 1 (September 5, 2022). http://dx.doi.org/10.1038/s41598-022-19022-w.
Повний текст джерелаАнотація:
AbstractCattle enterprises are one of the major livestock production systems globally and are forecasted to have stable growth in the next decade. To facilitate sustainable live weight production, optimal reproductive performance is essential. Microbial colonisation in the reproductive tract has been demonstrated as one of the factors contributing to bovine reproductive performance. Studies also implied that reproductive metagenomes are different at each stage of the estrous cycle. This study applied Oxford Nanopore Technologies’ adaptive long-read sequencing to profile the bovine reproductive microbiome collected from tropical cattle in northern Queensland, Australia. The microbiome samples were collected from cattle of different sexes, reproductive status and locations to provide a comprehensive view of the bovine reproductive microbiome in northern Australian cattle. Ascomycota, Firmicutes and Proteobacteria were abundant phyla identified in the bovine reproductive metagenomes of Australian cattle regardless of sexes, reproductive status and location. The species level taxonomical investigation suggested that gastrointestinal metagenome and the surrounding environment were potentially the origins of the bovine reproductive metagenome. Functional profiles further affirmed this implication, revealing that the reproductive metagenomes of the prepubertal and postpartum animals were dominated by microorganisms that catabolise dietary polysaccharides as an energy substrate while that of the pregnant animals had the function of harvesting energy from aromatic compounds. Bovine reproductive metagenome investigations can be employed to trace the origins of abnormal metagenomes, which is beneficial for disease prevention and control. Additionally, our results demonstrated different reproductive metagenome diversities between cattle from two different locations. The variation in diversity within one location can serve as the indicator of abnormal reproductive metagenome, but between locations inferences cannot be made. We suggest establishing localised metagenomic indices that can be used to infer abnormal reproductive metagenomes which contribute to abortion or sub-fertility.
23
Denamiel, Cléa, Xun Huan, and Ivica Vilibić. "Conceptual Design of Extreme Sea-Level Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods." Frontiers in Marine Science 8 (May 20, 2021). http://dx.doi.org/10.3389/fmars.2021.650279.
Повний текст джерелаАнотація:
Coastal hazards linked to extreme sea-level events are projected to have a direct impact (by flooding) on 630 million of people by year 2100. Numerous operational forecasts already provide coastal hazard assessments around the world. However, they are largely based on either deterministic tools (e.g., numerical ocean and atmospheric models) or ensemble approaches which are both highly demanding in terms of high-performance computing (HPC) resources. Through a robust learning process, we propose conceptual design of an innovative architecture for extreme sea-level early warning systems based on uncertainty quantification/reduction and optimization methods. This approach might be cost-effective in terms of real-time computational needs while maintaining reliability and trustworthiness of the hazard assessments. The proposed architecture relies on three main tools aligning numerical forecasts with observations: (1) surrogate models of extreme sea-levels using polynomial chaos expansion, Gaussian processes or machine learning, (2) fast data assimilation via Bayesian inference, and (3) optimal experimental design of the observational network. A surrogate model developed for meteotsunami events – i.e., atmospherically induced long ocean waves in a tsunami frequency band – has already been proven to greatly improve the reliability of extreme sea-level hazard assessments. Such an approach might be promising for several coastal hazards known to destructively impact the world coasts, like hurricanes or typhoons and seismic tsunamis.