A new iterative algorithm for probabilistic performance measure

Compared to traditional Reliability Index Approach (RIA), Performance Measure Approach (PMA) is considered to be more efficient and stable for evaluation of probabilistic constraints in reliability-based design optimization of structures. In PMA, the probabilistic performance measure is obtained through locating the minimum performance target point (MPTP) with the specified target reliability index in standard normal space. The advanced mean-value (AMV) method is well suitable for locating MPTP due to its simplicity and efficiency. However, the iterative sequence may converge very slowly, or oscillate and fail to converge if the performance function is highly nonlinear. Several modified algorithms were suggested to enhance the convergence of AMV, but their implementation is complicated and the prior knowledge of convexity or concavity of the performance function is needed. In this paper an easy iterative algorithm, which introduces a “new” step size to control the convergence of the sequence, is proposed. This step size is new because it may be constant during the iteration or decreases successively using a self-adjust strategy. It is proved that the AMV method is a special case of this proposed algorithm when the step size tends to infinity. Numerical results of several nonlinear performance functions indicate that the proposed algorithm is effective and as simple as the AMV but more robust

Determinants of future earnings: UK publicly listed companies

This paper tried to use cross-sectional models based on model developed by Hou, van Dijk and Zhang (2012) to forecast earnings of listed companies in the UK market. This paper has eight sections. The introduction mainly introduced the meaning of forecasting and methods to forecast. The literature review section introduced the research achievements and contributions made by researchers in forecasting area and how each forecasting methods used by scholars. In the research question and development of hypotheses section, I illustrated every hypotheses and how to test these hypotheses. The next section is data and empirical methodology, I explained how to choose and get data and how to select holdout sample and test sample. Besides, I introduced how to test and solve heteroscedasticity and how to test performs of model. The next section is analysis part. In this part I explained details of the summary of data (samples) and regression results firstly. Then I introduced test and solution about heteroscedasticity comprehensive, including White test and FGLS (Feasible Generalised Least Squares). Thirdly, I interpreted how to use several indictors to conduct errors analysis. After the errors analysis, I tried to use a new cross-sectional model to fit UK market better. The third last part is supplementary test. I used a deflated model to test if this model is better than the previous models. In the conclusion, I summarized this paper and introduced the limitation and directions for further researches. The last section is references

A Validation Approach to Over-parameterized Matrix and Image Recovery

In this paper, we study the problem of recovering a low-rank matrix from a number of noisy random linear measurements. We consider the setting where the rank of the ground-truth matrix is unknown a prior and use an overspecified factored representation of the matrix variable, where the global optimal solutions overfit and do not correspond to the underlying ground-truth. We then solve the associated nonconvex problem using gradient descent with small random initialization. We show that as long as the measurement operators satisfy the restricted isometry property (RIP) with its rank parameter scaling with the rank of ground-truth matrix rather than scaling with the overspecified matrix variable, gradient descent iterations are on a particular trajectory towards the ground-truth matrix and achieve nearly information-theoretically optimal recovery when stop appropriately. We then propose an efficient early stopping strategy based on the common hold-out method and show that it detects nearly optimal estimator provably. Moreover, experiments show that the proposed validation approach can also be efficiently used for image restoration with deep image prior which over-parameterizes an image with a deep network.Comment: 29 pages and 9 figure

Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process-based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a "space-for-time" substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m(3) ha(-1). The total timber production of LP was estimated to be 7.27 x 10(6) m(3), with 4.87 x 10(6) m(3) in current GSV and 2.40 x 10(6) m(3) in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from -55.2 to 56.3 m(3) ha(-1). The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service. (C) 2016 Elsevier B.V. All rights reserved.ArticleINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION.52:155-165(2016)journal articl

Are All Losses Created Equal: A Neural Collapse Perspective

While cross entropy (CE) is the most commonly used loss to train deep neural networks for classification tasks, many alternative losses have been developed to obtain better empirical performance. Among them, which one is the best to use is still a mystery, because there seem to be multiple factors affecting the answer, such as properties of the dataset, the choice of network architecture, and so on. This paper studies the choice of loss function by examining the last-layer features of deep networks, drawing inspiration from a recent line work showing that the global optimal solution of CE and mean-square-error (MSE) losses exhibits a Neural Collapse phenomenon. That is, for sufficiently large networks trained until convergence, (i) all features of the same class collapse to the corresponding class mean and (ii) the means associated with different classes are in a configuration where their pairwise distances are all equal and maximized. We extend such results and show through global solution and landscape analyses that a broad family of loss functions including commonly used label smoothing (LS) and focal loss (FL) exhibits Neural Collapse. Hence, all relevant losses(i.e., CE, LS, FL, MSE) produce equivalent features on training data. Based on the unconstrained feature model assumption, we provide either the global landscape analysis for LS loss or the local landscape analysis for FL loss and show that the (only!) global minimizers are neural collapse solutions, while all other critical points are strict saddles whose Hessian exhibit negative curvature directions either in the global scope for LS loss or in the local scope for FL loss near the optimal solution. The experiments further show that Neural Collapse features obtained from all relevant losses lead to largely identical performance on test data as well, provided that the network is sufficiently large and trained until convergence.Comment: 32 page, 10 figures, NeurIPS 202

Generalized Neural Collapse for a Large Number of Classes

Neural collapse provides an elegant mathematical characterization of learned last layer representations (a.k.a. features) and classifier weights in deep classification models. Such results not only provide insights but also motivate new techniques for improving practical deep models. However, most of the existing empirical and theoretical studies in neural collapse focus on the case that the number of classes is small relative to the dimension of the feature space. This paper extends neural collapse to cases where the number of classes are much larger than the dimension of feature space, which broadly occur for language models, retrieval systems, and face recognition applications. We show that the features and classifier exhibit a generalized neural collapse phenomenon, where the minimum one-vs-rest margins is maximized.We provide empirical study to verify the occurrence of generalized neural collapse in practical deep neural networks. Moreover, we provide theoretical study to show that the generalized neural collapse provably occurs under unconstrained feature model with spherical constraint, under certain technical conditions on feature dimension and number of classes.Comment: 32 pages, 12 figure

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms. Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance. Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/). Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC

Equipo simulador de agua de lluvia para experimentos a campo en ecosistemas áridos y semiáridos

The predicted changes in precipitation patterns because of global change have profound effects on terrestrial ecosystems. In the present study, the principle and design details of a rainfall enrichment system (RAINES) for open field experiments in semi-arid and arid ecosystems are presented. The rainfall intensity, validity and uniformity of this experimental facility were also tested. During the period from 2008 to 2010, our data showed that the RAINES was able to simulate rainfall events with different rainfall sizes, frequencies and timing. The greatest advantage of the RAINES was its high uniformity in rainfall distribution over a relatively large experimental surface area (>65 m2), which was important for experimental studies of semi-arid and arid ecosystems where vegetation distribution is sparse. The rainfall validity of RAINES was steadily at 66% or higher as long as the hydraulic pressure exceeded 1.4 KPa and the wind speed was below 2.5 m s-1. Since the RAINES is light-weight, inexpensive and versatile enough to be used to simulate various rainfall events with needed properties in remote fields, it is able to provide reliable simulated rainfall in the field for studying possible responses of soil and vegetation processes to rainfall change in arid and semi-arid ecosystems. The application of the RAINES will improve our understanding on the relationship between water availability and ecosystem processes in arid and semi-arid ecosystems, which will provide useful knowledge for the protection, restoration and sustainable management of semi-arid and arid desert ecosystems worldwide.Los cambios predichos en los modelos de precipitación como resultado del cambio global tienen efectos profundos en los ecosistemas terrestres. Un equipo que simule la lluvia es una herramienta de investigación efectiva para explorar los efectos de los cambios en los modelos de lluvia sin varias restricciones naturales. En el presente estudio, se presentan los principios y detalles de diseño de un equipo simulador de lluvia (RAINES) para estudios a campo en ecosistemas áridos y semiaridos. También se determinaron la intensidad de lluvia, validez y uniformidad del RAINES. Durante el período 2008 al 2010, nuestros datos mostraron que el RAINES fue capaz de simular eventos de lluvia con diferentes cantidades, frecuencias y momentos de lluvia. La mayor ventaja del RAINES fue su gran uniformidad en la distribución de la lluvia sobre una superficie experimental relativamente grande (>65 m2 ). Esto es importante para estudios experimentales en ecosistemas áridos y semiáridos donde la distribución de la vegetación es dispersa. La validez de la lluvia provista por el RAINES fue al menos de 66% siempre y cuando la presión hidráulica excedió los 1.4 KPa y la velocidad del viento fue menor que 2.5 m s-1. El RAINES es de bajo peso, no costoso y lo suficientemente versátil como para ser usado para simular varios eventos de lluvia bajo condiciones de campo distantes. Es capaz de proveer lluvia simulada en forma confiable en el campo para estudiar la respuesta de procesos en el suelo y la vegetación a cambios en la cantidad de lluvia en ecosistemas áridos y semiáridos. El uso del RAINES mejorará nuestro entendimiento en la relación entre la disponibilidad de agua y los procesos en ecosistemas áridos y semiáridos. También proveerá conocimiento útil para la protección, restauración y menejo sustentable de ecosistemas de desierto áridos y semiáridos a escala mundial.Fil: Xin, Zhiming. Chinese Academy of Forestry; ChinaFil: Qian, Jianqiang. Henan Agricultural University; ChinaFil: Busso, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: Wu, Bo. Chinese Academy of Forestry; ChinaFil: Zhu, Yajuan. Chinese Academy of Forestry; ChinaFil: Zhang, Jinxin. Chinese Academy of Forestry; ChinaFil: Li, Yonghua. Chinese Academy of Forestry; China. State Forestry Administration; ChinaFil: Lu, Qi. Chinese Academy of Forestry; China. State Forestry Administration; Chin

Spatial and temporal regeneration patterns within gaps in the primary forests vs. secondary forests of Northeast China

Forest gaps play an important role during forest succession in temperate forest ecosystems. However, the differences in spatial distribution and replacement patterns of woody plants (trees and shrubs) between primary and secondary forests remain unclear during the gap-filling processes, especially for temperate forests in Northeast China. We recorded 45,619 regenerated trees and shrubs in young gaps (<10 years), old gaps (10~20 years), and closed forest stands (i.e., filled gaps) in the primary broadleaved Korean pine (Pinus koraiensis Sieb. Rt Zucc.) forests vs. secondary forests (degraded from primary forests). The gap-filling processes along horizontal (Cartesian coordinate system) and vertical (lower layer: 0~5 m, medium layer: 5~10 m, and upper layer: >10 m) dimensions were quantified by shade tolerance groups of trees and shrubs. We found that gap age, competition between species, and pre-existing regeneration status resulted in different species replacement patterns within gaps in primary vs. secondary forests. Gap formation in both primary and secondary forests increased species richness, with 33, 38, 39, and 41 in the primary closed stands, primary forest gaps, secondary closed stands, and secondary forest gaps, respectively. However, only 35.9% of species in primary forest gaps and 34.1% in secondary forest gaps successfully reached the upper layer. Based on the importance values (IVs) of tree species across different canopy heights, light-demanding trees in the upper layer of the secondary forests were gradually replaced by intermediate and shade-tolerant trees. In the primary forests, Korean pine exhibited intermittent growth patterns at different canopy heights, while it had continuous regeneration along vertical height gradients in the secondary forests. The differences in Korean pine regeneration between the primary and secondary forests existed before gap formation and continued during the gap-filling processes. The interspecific competition among different tree species gradually decreased with increasing vertical height, and compared to the primary forests, the secondary forests showed an earlier occurrence of competition exclusion within gaps. Our findings revealed the species replacement patterns within gaps and provided a further understanding of the competition dynamics among tree species during the gap-filling processes

A rainfall enrichment system suitable for open field experiments in arid and semi-arid ecosystems

The predicted changes in precipitation patterns because of global change have profound effects on terrestrial ecosystems. In the present study, the principle and design details of a rainfall enrichment system (RAINES) for open field experiments in semi-arid and arid ecosystems are shown. The rainfall intensity, validity and uniformity of this experimental facility were also tested. During the period from 2008 to 2010, our data showed that the RAINES was able to simulate rainfall events with different rainfall sizes, frequencies and timing. The greatest advantage of the RAINES was its high uniformity in rainfall distribution over a relatively large experimental surface area (>90 m2), which was important for experimental studies of semi-arid and arid ecosystems where vegetation distribution is sparse. The rainfall validity of RAINES was steadily at 66% or higher as long as the hydraulic pressure exceeded 1.4 KPa and the wind speed was below 2.5 m s-1. Since the RAINES is light-weight, inexpensive and versatile enough to be used to simulate various rainfall events with needed properties in remote fields, it is able to provide reliable simulated rainfall in the field for studying possible responses of soil and vegetation processes to rainfall change in arid and semi-arid ecosystems. The application of the RAINES will improve our understanding on the relationship between water availability and ecosystem processes in arid and semi-arid ecosystems, which will provide useful knowledge for the protection, restoration and sustainable management of semi-arid and arid desert ecosystems world