Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) (...) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. (shrink)

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can (...) feed information into the approach, appropriate to different collections of models. (shrink)

As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...) sound methodological basis would imply that climate simulations which are based on complex climate models had, in stark contrast to their current hegemony, hardly an epistemic role to play in climate scenario analysis at all. Their main function might actually consist in ‘foreseeing future ozone-holes’. In order to argue for these theses, I explain first of all the plurality of climate models used in climate science by the failure to avoid the problem of underdetermination. As a consequence, climate simulation results have to be interpreted as modal sentences, stating what is possibly true of our climate system. This indicates that climate policy decisions are decisions under uncertainty. Two general methodological principles which may guide the construction of the scenario range are formulated and contrasted with each other: modal inductivism and modal falsificationism. I argue that modal inductivism, being the methodology implicitly underlying the third IPCC report, is severely flawed. Modal falsificationism, representing the sound alternative, would in turn require an overhaul of the IPCC practice. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...) product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

In this theoretical contribution, we interpret the notion of professional development in the light of concepts coming from cognitive development. This brings us to consider teacher training as not only having to allow the elaboration of skills, but also as having to encourage the development of new cognitive structures. We explore the theoretical concepts in relation with certain processes revealed by the reflexive texts of the students. Finally, we propose ways that could foster certain dimensions of global professional development in (...) learners, among which self-regulated learning, the focus on certain concerns and taking into account certain types of information. (shrink)

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it (...) is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation. (shrink)

Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study (...) developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance. (shrink)

Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact on the work attention not only for the mobile phone owners, but also for the surrounding people. Decision tree is the most popular machine-learning classification technique that is used in existing context-aware mobile intelligent interruption management model to overcome such issues. However, a single-decision tree-based context-aware model may cause over-fitting problem (...) and thus decrease the prediction accuracy of the inferred model. Therefore, in this paper, we propose an ensemble machine-learning-based context-aware mobile telephony model for the purpose of intelligent interruption management by taking into account multi-dimensional contexts and name it “E-MIIM”. The experimental results on individuals’ real-life mobile telephony data sets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual’s mobile telephony interruptions based on their relevant contextual information. (shrink)

Today’s most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of climate change, highlighting requirements that I refer to as ownership, justification, and robustness.

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the (...) generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles — parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. (shrink)

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine, adaptive neurofuzzy inference system, feed-forward neural network, and one conventional multilinear regression modes, were (...) used for SSL modeling. Besides, four different ensemble methods, neural network ensemble, ANFIS ensemble, weighted average ensemble, and simple average ensemble, were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year discharge and SSL data for training and verification of the applied models. Determination coefficient and root mean square error were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. (...) Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. (...) For the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. (shrink)

One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system (...) as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains. (shrink)

Forecasting blood glucose values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network, (...) which is optimized for sequence data, and RNN-based algorithms, stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm was applied to the five prediction models to optimize their weights through ensemble techniques and to yield the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08, 19.25, and 31.30 mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28, 19.99, and 33.13 mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance. (shrink)

Epidemiological models of the transmission of SARS-CoV-2 played an important role in guiding the decisions of policy-makers during the pandemic. Such models provide output projections, in the form of time -series of infections, hospitalisations, and deaths, under various different parameter and scenario assumptions. In this paper I caution against handling these outputs uncritically: raw model-outputs should not be presented as direct projections in contexts where modelling results are required to support policy -decisions. I argue that model uncertainty should (...) be handled and communicated transparently. Drawing on methods used by climate scientists in the fifth IPCC report I suggest that this can be done by: attaching confidence judgements to projections based on model results; being transparent about how multi-model ensembles are supposed to deal with such uncertainty; and using expert judgement to ‘translate’ model-outputs into projections about the actual world. In a slogan: tell me what you think (and why), not (just) what your models say. I then diffuse the worry that this approach infects model-based policy advice with some undesirably subjective elements, and explore how my discussion fares if one thinks the role of a scientific advisor is to prompt action, rather than communicate information. (shrink)

Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting (...) to show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case. (shrink)

The financial market is a highly complex and dynamic system that has great commercial value; thus, many financial elite are drawn to research on the subject. Recent studies show that machine learning methods perform better than traditional statistical ones. In our study, based on the characteristics of financial sequence data, we propose a wrapped ensemble approach using a supervised learning algorithm to predict stock price volatility of China’s stock markets. To check our new approach, we developed an intelligent financial forecast (...) system and used the Hushen 300 index data to test our model; it proves that our model performs better than a single algorithm. We also compared our model with the famous ensemble approach of bagging, and the result shows that our model is better. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that climate science (...) has made improvements in its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

. This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset. The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along (...) with five chosen base classifiers, namely support vector machine, radial basis function network, decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously considered (...) as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Model evaluation for long term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on (...) model democracy, yet there are open questions on what constitutes a “good” model, how to define dependency, how to interpret robustness, and how to incorporate background knowledge. Under¬standing those issues has the potential to increase confidence in model predictions in modeling efforts outside of climate science where similar challenges exist. (shrink)

We discuss two models of virtue cultivation that are present throughout the Republic: the self-mastery model and the harmony model. Schultz (2013) discusses them at length in her recent book, Plato’s Socrates as Narrator: A Philosophical Muse. We bring this Socratic distinction into conversation with two modes of intentional regulation strategies articulated by James J. Gross. These strategies are expressive suppression and cognitive reappraisal. We argue that that the Socratic distinction helps us see the value in cognitive reappraisal (...) and that the contemporary neurological research supports the wide range of attitudes toward the value of emotional experience that mirror those found in the Republic. (shrink)

José A. Díez examines the reasons for claiming that models involve fictions. He opposes the claim that, in order to account for some key features of the practice of modeling in science, such as the existence of unsuccessful representations and also of successful yet inaccurate or idealized ones, it is necessary to accept fictional entities. In resisting such a view, he sketches an account of scientific modeling and argue that according to such account there is no need for strong factionalism, (...) only a weak, unproblematic fictionalist component is needed. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Embodied Knowledge in Ensemble Performance by J. Murphy McCalebEric C. Melley, D.M.A.J. Murphy McCaleb, Embodied Knowledge in Ensemble Performance (Surrey, England: Ashgate, 2014)J. Murphy McCaleb’s Embodied Knowledge in Ensemble Performance explores how musicians interact and share information while performing, specifically within unconducted chamber ensembles. The book is a direct outgrowth of the author’s doctoral dissertation and follows a similar format. Beginning with a presentation of four essential (...) research questions, McCaleb continues with an overview and justification for his methodology, followed by case studies, conclusions, and foundations for further research. The four questions McCaleb seeks to answer with this volume are: How do musicians interact and share information with each other while performing? What is the nature of the information being shared in ensemble performance? To what extent does the musical content being performed affect the ways it has to be physically created by musicians? How does the physical relationship between the performers and their instruments relate to communicative and interactive processes of ensemble performance? After an explanation of methodology, McCaleb devotes a chapter to each of the four questions. The substantial and excellent review of literature is distributed throughout the comprising six chapters, providing a natural connection between case studies, previous research, and resultant conclusions. While the author suggests the book [End Page 103] has value for both researchers and practitioners, the most prominent result of the discourse provides a new paradigm with which to study ensemble interaction, but contains few practical suggestions for performers, leaning heavily on previous research. McCaleb’s work will most directly benefit other researchers and music education theorists.The volume spans 140 pages, including an index and bibliography, as well as an accompanying DVD. The DVD includes video examples of rehearsals and performances for two ensembles that served as subjects of the research case studies: the Boult String Quartet, the premiere student quartet at the Birmingham Conservatoire (where the author was completing his doctorate), and The Supergroup, an ensemble that performs free group improvisations. The author is himself a member of The Supergroup and serves as “a reflective practitioner, assuming the roles of researcher and musician” (p.16). While his research methodology is pluralistic (though decidedly qualitative in nature), the employment of action research, wherein the “people being studied become part of the knowledge creation process,” is a prominent element in his method (p. 13). This is one area where McCaleb’s perspective is both powerful and open to debate. One of the central and strongest premises of McCaleb’s work is that music is a singular form of procedural knowledge. Because the nature of procedural knowledge at some level defies linguistic translation, the perspective of performing musicians is a critical element to any phenomenological study of musical performance. The author acknowledges that this raises questions of subjectivity and works to incorporate empirical methods such as “observation, interviews, literature review, and case studies” to counter the more subjective methodology (p.15). McCaleb’s multifaceted approach to research is for the most part highly effective, but at times the anecdotal tone of his first-hand descriptions of musical experiences can soften the force of his premise.Based on the concept that music is a form of procedural knowledge, McCaleb posits that communication among ensemble musicians is first and foremost musical. In order to illustrate this, the author explores other common models used to study interaction between musicians. In this review of other research studies, McCaleb is most effective in pointing out the inadequacies of applying other models to musical ensemble interaction. The linguistic model, which seeks to correlate physical gestures with speech patterns, remains inadequate to study communication between instrumental musicians. Previous research investigating the gestural model, while focusing on instrumental music, is preoccupied with emotional content, primarily between performer and audience, and does not approach the question of what and how communication occurs between performing musicians (pp. 27–28). Based on the inadequacies of other research [End Page 104] models, McCaleb seeks to establish a new paradigm of interaction entirely independent of social models and based solely in musical performance (p. 7).This points to an important area of research that McCaleb largely neglects: the neuroscience... (shrink)

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance (...) the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID. (shrink)

Residual fluctuations produced in typical experimental methodologies are examined as correlated noises. The effective range of the correlations was assessed by determining whether the decay over look‐back time is better described as a power law or exponential. Both of these decay laws contain free parameters and it is argued that it is not possible to distinguish their models on the basis of simple measures of goodness‐of‐fit. Global analyses that evaluate models on the basis of how well they generalize are conducted. (...) The models are examined in terms of three constructs that all bear on generalization: cross‐validity, flexibility, and representativeness. Quantitative assessment of a large ensemble of data suggests that the correlations decay over time as a power law. The conclusion is that human residual fluctuation is a correlated fractal. (shrink)

Simulation-based weather and climate prediction now involves the use of methods that reflect a deep concern with uncertainty. These methods, known as ensemble prediction methods, produce multiple simulations for predictive periods of interest, using different initial conditions, parameter values and/or model structures. This paper provides a non-technical overview of current ensemble methods and considers how the results of studies employing these methods should be interpreted, paying special attention to probabilistic interpretations. A key conclusion is that, while complicated inductive arguments (...) might be given for the trustworthiness of probabilistic weather forecasts obtained from ensemble studies, analogous arguments are out of reach in the case of long-term climate prediction. In light of this, the paper considers how predictive uncertainty should be conveyed to decision makers. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase (...) in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Les considérations physiologiques sont étrangères, en tant que telles, au projet de l'Ethique, et sans doute, à l'ensemble des préoccupations philosophiques de Spinoza. Au début de Ia seconde partie de l'Ethique, Spinoza précise clairement: “j'expliquerai seulement ce qui peut nous conduire comme par la main à la connaissance de l’ Arne humaine et de sa béatitude suprême”. Pourtant, le livre ne laisse pas de contenir une révision intéressante du modèle mécaniste que Descartes appliquait à l'explication du corps humain; il contient (...) surtout une hypothèse sur Ia structure organique, qui, nous le croyons, a joué un rôle de modèle pour un certain type de théorie de l'être vivant. Ce type de conception se trouve d'abord dans l'iatromécanisme de Boerhaave et de ses disciples au XVIIIe siècle, mais les séquelles en sont identifiables jusque dans Ia philosophie biologique contemporaine. (shrink)

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project. We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more (...) naïve predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian, and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work. (shrink)

Ce travail présente quelques modèles mathématiques de répartition d'objets dans un ensemble de cases devant servir en particulier à l'analyse des résultats expérimentaux concernant la distribution des œufs d'un parasite dans un groupe de ses hôtes. On trouvera successivement une descriptions des situations concrêtes permettant l'utilisation de tels modèles, un rappel sur le matériel d'analyse combinatoire utilisé, cinq modèles relatifs aux différentes hypothèses de distribution au hasard, réception au hasard, reparasitisme à probabilités constante et variable et un exemple d'application aux (...) résultats de V.Labeyrie sur le parasitime d'Acrolepia assectella Z. par un ichneumonide Several mathematical models of distribution of objects among a set of boxes are presented in view of the analysis of experimental results obtained on the distribution of the eggs of a parasite among a group of its hosts.First, practical situations which allow the application of such models are described. In a second part, one recalls the elements of combinatorial analysis required by the following mathematical developments.Five models are then set up, each one being associated with the different hypothesis of: chance distribution, chance acceptance, re-parasitism with constant or variable probability Finally, an exemple of application to the results of V.Labeyrie on the parasitism ofAcrolepia assectella Z. by an ichneumon fly is given.Diese Arbeit soll einige mathematische Modelle von Verteilung der Gegenstände in eine Menge von Fächern geben, deren Zweck im Besonderen die Analyse von Versuchsergebnissen über die Eierlegung eines Parasiten in eine Gruppe seiner Wirte sein will.Es folgen: eine Beschreibung der konkreten Lagen, die eine Verwendung solcher Modelle ermöglichen, eine Aufzählung der benutzten Ergebnisse kombinatorischer Analysis, fünf Modelle, die folgenden verschiedenen Voraüssetzungen entsprechen: Zufallverteilung, Zufallaufnahme, wiederholter Parasitismus mit stetiger oder veränderlicher Wahrscheinlichkeit und ein Anwendungsbeispiel an Ergebnisse von V.Labeyrie über Parasitismus derAcrolepia assectella Z. durch einen Ichneumoniden—. (shrink)

Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed determining the local trends of precipitations, investigating precipitation nonlinear (...) dynamics, and assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, α 0, and the width of the spectrum w, consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation. Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data. (shrink)

'Mass terms', words like water, rice and traffic, have proved very difficult to accommodate in any theory of meaning since, unlike count nouns such as house or dog, they cannot be viewed as part of a logical set and differ in their grammatical properties. In this study, motivated by the need to design a computer program for understanding natural language utterances incorporating mass terms, Harry Bunt provides a thorough analysis of the problem and offers an original and detailed solution. An (...) extension of classical set theory, Ensemble Theory, is defined, and this provides the conceptual basis of a framework for the analysis of natural language meaning which Dr Bunt calls Two-level model-theoretic semantics. The validity of the framework is convincingly demonstrated by the formal analysis of a fragment of English including sentences with quantified and modified mass terms. Separate chapters of the book are devoted to an axiomatic definition of Ensemble Theory and a detailed discussion of its status as a mathematical formalism. (shrink)

Background. Imposter syndrome, associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students’ IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such (...) as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction. (shrink)

It is shown that the matrix models which give non-perturbative definitions of string and M theory may be interpreted as non-local hidden variables theories in which the quantum observables are the eigenvalues of the matrices while their entries are the non-local hidden variables. This is shown by studying the bosonic matrix model at finite temperature, with T taken to scale as 1/N, with N the rank of the matrices. For large N the eigenvalues of the matrices undergo Brownian motion (...) due to the interaction of the diagonal elements with the off diagonal elements, giving rise to a diffusion constant that remains finite as N goes to infinity. The resulting probability density and current for the eigenvalues are then found to evolve in agreement with the Schroedinger equation, to leading order in 1/N, with hbar proportional to the thermal diffusion constant for the eigenvalues. The quantum uctuations and uncertainties in the eigenvalues are then consequences of ordinary statistical uctuations in the values of the off-diagonal matrix elements. Furthermore, this formulation of the quantum theory is background independent, as the definition of the thermal ensemble makes no use of a particular classical solution. The derivation relies on Nelson's stochastic formulation of quantum theory, which is expressed in terms of a variational principle. (shrink)

Le but de cet article est de présenter un modèle formel des processus dichotomiques platoniciens. Cette méthode, déjà utilisée dans le Gorgias et décrite dans le Phèdre, reçoit une grande extension dans les dialogues ultérieurs. Elle s'efforce d'obtenir une définition à partir des divisions successives d'un ensemble de concepts. Nous montrons que les chaînes de dichotomies ne fonctionnent pas comme des classifications, mais comme des « filtres convergents » sur l'espace des Idées. Cela veut dire que cet espace est, formellement (...) parlant, un espace topologique compact, donc fini. Mais, dans d'autres textes, Platon semble indiquer que les Idées sont en nombre infini. Dans cette hypothèse, il resterait possible d'atteindre, au moins à l'infini, quelque chose comme une définition, en considérant l'Idée du Bien comme une sorte de principe de compactification de l'univers platonicien. The purpose of this article is to present a formal model of platonician dichotomic processes. This method, already used in Gorgias and described in Phaedrus, takes a great development in further dialogues. It tries to get a definition from successive divisions of a set of concepts. We show that the chains of dichotomies do not work as classifications but as "convergent filters "on the space of Ideas. It means that this space is, formally speaking, a compact and therefore, finite, topological space. But in some other texts, Platon seems to specify that there is an infinite number of Ideas. In this case, it will remain possible to reach, at least in the infinite, something like a definition, by regarding the Idea of Good as a sort of compactness principle of the platonician universe. (shrink)

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging ), boosting and extreme gradient boosting regression ), and stacking are employed as ensemble models. Different machine learning approaches, including support vector regression, extreme learning machine, and multilayer perceptron neural network, are adopted as reference models. In order to maximize the determination coefficient value and reduce the root mean square error, hyperparameters are set using (...) the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 and STACK2 models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead. (shrink)

The aim of this thesis is to improve our understanding of how to assess and communicate uncertainty in areas of research deeply afflicted by it, the assessment and communication of which are made more fraught still by the studies’ immediate policy implications. The IPCC is my case study throughout the thesis, which consists of three parts. In Part 1, I offer a thorough diagnosis of conceptual problems faced by the IPCC uncertainty framework. The main problem I discuss is the persistent (...) ambiguity surrounding the concepts of ‘confidence’ and ‘likelihood’; I argue that the lack of a conceptually valid interpretation of these concepts compatible with the IPCC uncertainty guide’s recommendations has worrying implications for both the IPCC authors’ treatment of uncertainties and the interpretability of the information provided in the AR5. Finally, I show that an understanding of the reasons behind the IPCC’s decision to include two uncertainty scales can offer insights into the nature of this problem. In Part 2, I review what philosophers have said about model-based robustness analysis. I assess several arguments that have been offered for its epistemic import and relate this discussion to the context of climate model ensembles. I also discuss various measures of independence in the climate literature, and assess the extent to which these measures can help evaluate the epistemic import of model robustness. In Part 3, I explore the notion of the ‘weight of evidence’ typically associated with Keynes. I argue that the Bayesian is bound to struggle with this notion, and draw some lessons from this fact. Finally, I critically assess some recent proposals for a new IPCC uncertainty framework that significantly depart from the current one. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of (...) irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)