Precise global solar radiation data are indispensable to the design, planning, operation, and management of solar radiation utilization equipment. Some examples prove that the uncertainty of the prediction of solar radiation provides more value than deterministic ones in the management of power systems. This study appraises the potential of random forest, V-support vector regression, and a resilient backpropagation artificial neural network for daily global solar radiation point prediction from average relative humidity, daily average temperature, and daily sunshine duration. To acquire (...) more accurate predictions of DGSR and examine the influence of historical DGSR on the performance of point prediction models, two different model inputs are considered: three meteorological variables and the lags of DGSR and three meteorological variables. Then, two interval prediction methods are developed by introducing the KDE to out-of-bag, introducing kernel density estimation to split conformal based on the three machine learning models. The two methods for interval prediction are denoted as OOB-KDE and SC-KDE. The mean absolute error, mean relative error, and Kendall rank correlation are used to assess the point prediction models. The performance of interval prediction methods is evaluated by the prediction interval coverage probability, prediction interval normalized average width, and coverage width criteria. The following conclusions are drawn from this study. First, the V-SVR model performs best with the lowest mean absolute error of 0.016 and mean relative error of 0.001. Second, the lags of DGSR improve the prediction accuracy by about 30%. Third, the OOB-KDE and SC-KDE methods improved the quality of the prediction interval. OOB-KDE improved CWC by 81%, and SC-KDE improved CWC by 99.99%. Fourth, the best interval prediction result is obtained using the SC-KDE method using the V-SVR model. The average difference between its PICP and prediction interval nominal coverage is only 3% of the PINC, and its PINAW is less than 0.007. (shrink)

Linear regression and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative ε dragging linear regression method for robust classification on noised and (...) nonlinear data. First, a technique called negative ε dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision. (shrink)

The mathematical model of physical problems interprets physical phenomena closely. This research work is focused on numerical solution of a nonlinear mathematical model of fractional Maxwell nanofluid with the finite difference element method. Addition of nanoparticles in base fluids such as water, sodium alginate, kerosene oil, and engine oil is observed, and velocity profile and heat transfer energy profile of solutions are investigated. The finite difference method involving the discretization of time and distance parameters is applied for numerical results by (...) using the Caputo time fractional operator. These results are plotted against different physical parameters under the effects of magnetic field. These results depicts that a slight decrease occurs for velocity for a high value of Reynolds number, while a small value of Re provides more dominant effects on velocity and temperature profile. It is observed that fractional parameters α and β show inverse behavior against u y, t and θ y, t. An increase in volumetric fraction of nanoparticles in base fluids decreases the temperature profile of fractional Maxwell nanofluids. Using mathematical software of MAPLE, codes are developed and executed to obtain these results. (shrink)

Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called (...) kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models. (shrink)

Facial expression recognition plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning model using the kernel entropy component analysis network and directed acyclic graph support vector machine. We use the (...) KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate. (shrink)

It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jäkel, Schölkopf, & Wichmann, 2009). We point out that ‘‘String kernels,’’ initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal for how the brain encodes orthographic information during reading. We suggest some reasons for this connection and we derive new ideas for visual word recognition that are successfully put (...) to the test. We argue that the versatility and performance of String kernels makes a compelling case for their implementation in the brain. (shrink)

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine classifier to control a robot arm in order to help a person to find an object in the environment. (...) The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform and the empirical modal decomposition, combined with the principal component analysis that were fed through a linear and radial basis function kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction. (shrink)

We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning, developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies ridges of the overlapped multicomponent from a time-frequency representation. The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs (...) first extraction is decided using the framework of the intrinsic mode polynomial chirp transform, which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications. (shrink)

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into (...) multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)

Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database (TMAD). After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering (KFCM) approach along with the edge bridge (...) and fill technique (EBFT). It was a flexible high-level machine learning technique to localize the object in a complex template. The experimental result shows that the proposed approach segments the normal and abnormal cancer regions by means of precision, recall, specificity, accuracy, and Jaccard coefficient. The proposed methodology improved the classification accuracy in lung cancer segmentation up to 2.5–5% compared to the existing methods deep convolutional neural network (DCNN) and diffusion-weighted approach. (shrink)

This study employs a newly developed methodology called the variational homotopy perturbation transformation method to study fractional acoustic wave equations. The motivation for this study is to extend the variational homotopy perturbation technique to the variational homotopy perturbation transformation technique in the sense of the Yang–Caputo–Fabrizio operator. The suggested method demonstrated a straightforward and accurate technique for investigating fractional-order partial differential equations. The technique’s validity is demonstrated through the use of several illustrative instances. The obtained answers were found to be (...) extremely near to the precise solutions. Additionally, the proposed strategy achieves the best degree of accuracy. Indeed, the current technique can be seen as one of the analytic strategies for solving nonlinear fractional partial differential equations compared to other analytical techniques. (shrink)

In this work, ZZ transformation is combined with the Adomian decomposition method to solve the dynamical system of fractional order. The derivative of fractional order is represented in the Atangana–Baleanu derivative. The numerical examples are combined for their approximate-analytical solution. It is explored using graphs that indicate that the actual and approximation results are close to each other, demonstrating the method’s usefulness. Fractional-order solutions are the most in line with the dynamics of the targeted problems, and they provide an endless (...) number of options for an optimal mathematical model solution for a particular physical phenomenon. This analytical approach produces a series form solution that is quickly convergent to exact solutions. The acquired results suggest that the novel analytical solution technique is simple to use and very successful at assessing complicated problems that arise in related fields of research and technology. (shrink)

Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix (...) estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. (shrink)

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative aided (...) diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors. (shrink)

Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it (...) is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy. (shrink)

The Motoneurone and its Muscle Fibres presents a state-of-the-art summary of knowledge concerning the motoneurones, vital for innervating and commanding skeletal muscles. No muscle action would be possible without motoneurones. These cells are therefore absolutely essential for the execution of normal behaviour and for life support. It is their degeneration that leads to various kinds of motoneurone disease that are often ultimately lethal. However, the study of motoneurones is also important for general insights as to how neurones work, because the (...) motoneurone is probably the best understood kind of nerve cell so far in neuroscience. Motoneurones of the spinal cord were the first type of central nerve cell to be subjected to detailed physiological measurements, and much is known about how their activity is regulated by synapses from other central neurones. For most of the individual neurones within the central nervous system, the precise functional tasks are difficult to define. However, for motoneurones much is now known about their short- and long-term interactions with their main targets, the skeletal muscle fibres. Functions of neurones must be analyzed in relation to the response properties of their target cells. Therefore, this book deals with both, summarizing classical as well as recent knowledge concerning the motoneurone and its muscle fibres. This is the first time that so many aspects of this broad subject matter are treated in one comprehensive monograph. (shrink)

We propose a kernel method for using combinations of features across example pairs in learning pairwise classifiers. Pairwise classifiers, which identify whether two examples belong to the same class or not, are important components in duplicate detection, entity matching, and other clustering applications. Existing methods for learning pairwise classifiers from labeled training data are based on string edit distance or common features between two examples. However, if two examples from the same class have few common features, these (...) class='Hi'>methods have difficulties in finding these pairs and achieving high recall. One typical example is to check whether two abbreviated author names in different citations refer to the same person or not. Since similarities between examples from the same class become close to zero, classifiers fail to distinguish positive pairs from negative pairs. One approach to avoiding the problem of zero similarities is using conjunctions of different features across examples, but implementing this idea straightforwardly makes the computational cost prohibitive for practical problems. Using a kernel on pair instances, our method can use feature conjunctions across examples without actually doing feature mappings, which are computationally expensive. The kernel is a tensor product of two inner products on the original feature space. The corresponding feature mapping generates conjunctions of features only across the two different examples while that of the conventional polynomial kernel also generates conjunctions of features from the same example, which are irrelevant to pairwise classification and cause deterioration of accuracy. Our experiments on the author matching problem show that this method can give a precision 4 to 8 times higher than that of previous methods at medium recall levels. (shrink)

The application of kernel methods to citation analysis is explored. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, global importance of individual documents, and importance of documents relative to one or more documents. The framework provided by the kernels establishes relative importance as an intermediate between relatedness and global importance, in which the degree of `relativity,' or the bias between relatedness (...) and importance, is naturally controlled by a parameter characterizing individual kernels in the family. (shrink)

The figures visualizing single and combined classifiers coming from decision trees group and Bayesian parametric and nonparametric discriminant functions show the importance of diversity of bagging or boosting combined models and confirm some theoretical outcomes suggested by other authors. For the three medical sets examined, decision trees, as well as linear and quadratic discriminant functions are useful for bagging and boosting. Classifiers, which do not show an increasing tendency for resubstitution errors in subsequent boosting deterministic procedures loops, are not useful (...) for fusion, e.g. kernel discriminant function. For the success of resampling classifiers’ fusion, the compromise be- tween accuracy and diversity is needed. Diversity important in the success of boosting and bagging may be assessed by concordance of base classifiers with the learning vector. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem formalization, model, prediction, (...) and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (shrink)

By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution (...) class='Hi'>kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein. (shrink)

In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes (...) an optimization method for finding the kernel radial basis function parameters based on improving the innovative gunner algorithm. As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%. (shrink)

In Karl Marx’s “Preface” to the second edition of Capital, Volume 1, he famously wrote that with Hegel dialectical thinking is “standing on its head. It must be turned right side up again, if you would discover the rational kernel within the mystical shell.” Unfortunately, across a wide spectrum of interpretations of Marxism, there continues to be a great deal of confusion about what Marx means by the “rational kernel” that he discerns within the Hegelian “mystical shell.” But (...) not just a great deal of confusion, but real mystification and distortion of what Marx himself means by dialectical thinking, and especially what a dialectical mode of explanation involves. The concern of this brief paper is to offer some considerations that might open up a clearer conceptual horizon for understanding Marx’s method of dialectical explanation, and the fundamental canons of interpretation that are associated with it. (shrink)

We introduce a new convolution kernel for labeled ordered trees with arbitrary subgraph features, and an efficient algorithm for computing the kernel with the same time complexity as that of the parse tree kernel. The proposed kernel is extended to allow mutations of labels and structures without increasing the order of computation time. Moreover, as a limit of generalization of the tree kernels, we show a hardness result in computing kernels for unordered rooted labeled trees with (...) arbitrary subgraph features. (shrink)

Graphical models are a powerful tool for causal model specification. Besides allowing for a hierarchical representation of variable interactions, they do not require any a priori specification of the functional dependence between variables. The construction of such graphs hence often relies on the mere testing of whether or not model variables are marginally or conditionally independent. The identification of causal relationships then solely requires some general assumptions on the relation between stochastic and causal independence, such as the Causal Markov Condition (...) and the Faithfulness Condition (Spirtes et al. 2000; Pearl 2000). However, a procedure would require further assumptions to hold. Namely those the independence tests themselves are based on. In continuous settings, Spirtes et al. (2000) suggest causal inference based on a very restrictive formulation of independence, that is, vanishing partial correlations. Such a measure does, however, limit the applicability of causal inference to linear systems. This constitutes a serious drawback especially for the social sciences where an a priori specification of the functional form proves difficult or at odds with linearity. In short: graphical models theoretically reduce specification uncertainty regarding functional dependence, but their implementation in practice deprives them of this virtue. In this paper we investigate how causal structures in continuous settings can be identified when both functional forms and probability distributions of the variables remain unspecified. We focus on tests exploiting the fact that if X and Y are conditionally independent given a set of variables Z, the two conditional densities f (X|Y, Z) and f (X|Z) must coincide. We start by estimating the conditional densities f(X|Y, Z) and f(X|Z) via nonparametric techniques (kernel methods). We proceed by testing if some metric expressing the distance between these very conditional densities is sufficiently close to zero. Out of several metrics available in the literature to express such distance we choose two, the Euclidean, and the Hellinger distance. We investigate in a Monte Carlo study how different tests involving either measure are able to detect statistical independence, conditioned on a small set of variables. One limitation may result from nonparametric density estimation being subject to the curse of dimensionality. As the number of variables increases, the estimated empirical density converges at a slower rate to its population value. To compensate this drawback we use a local bootstrap procedure which consists of resampling the data for each test. While local bootstrap strongly increases the computational time of the test, it succeeds in counterbalancing the curse of dimensionality. (shrink)

Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel density estimator. We also propose an application of (...) crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures. (shrink)

Because methodical doubt is a process of demarcation of scientific (clear) and non-scientific constructions, then in this process Descartes affirmed truly neo-positivistic principle. Descartes` rational transformation of thinking is usage methods of mechanical sciences to «sciences about spirit» attaching to them also natural status. But Descartes had not noticed that scientific obviousnesses with time turns into dogmas. That is why Husserl offered to describe phenomena after riching «epoche» about natural-scientific beliefs. Search of pretheoretical grounds of obviousness has led to (...) loss of clarity and of its apodictic weight. Because «obviousnesses» were always founded on historico-philosophical invariant, then needed either reconsider Descartes «technique» or kernel of decomposition and reconstruct «laws of thinking» in respect to it. (shrink)

Lean is a new open source theorem prover being developed at Microsoft Research and Carnegie Mellon University, with a small trusted kernel based on dependent type theory. It aims to bridge the gap between interactive and automated theorem proving, by situating automated tools and methods in a framework that supports user interaction and the construction of fully specified axiomatic proofs. Lean is an ongoing and long-term effort, but it already provides many useful components, integrated development environments, and a (...) rich API which can be used to embed it into other systems. It is currently being used to formalize category theory, homotopy type theory, and abstract algebra. We describe the project goals, system architecture, and main features, and we discuss applications and continuing work. (shrink)

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet and principal component analysis network is proposed. First, the data on structural magnetic resonance imaging and functional magnetic resonance (...) imaging are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional ShuffleNet is developed three-dimensional, which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines method classifier, which proves the feasibility and effectiveness of the proposed method. (shrink)

In his Populist Democracy: A Defence (1993), Torbjörn Tännsjö suggests, roughly, the following necessary and sufficient conditions for a democratic collective choice: If the majority of a given group of voters prefer A to B, then the collective choice is A rather than B; and if the majority of voters had preferred B to A, then the collective choice would have been B rather than A. Moreover, the preference of a voter is equated with the one she is showing by (...) the act of voting (e.g., by putting a ballot in a box). Tännsjö’s definition has the advantage of being simple, naturalistic, and, to the best of my knowledge, more exact that most other definitions proposed in the literature. As such, I think it is a useful starting point for a discussion of how to define democratic decision making. As we will see below, such a discussion can be quite illuminating and generate interesting and fruitful questions regarding how to understand democracy. Tännsjö claims that his definition is in line with common language use, i.e., that it is a lexical definition. Pace Tännsjö, I shall show that it departs from common language use since it has a number of counterintuitive implications in regards to which decisions and methods that are classified as democratic or undemocratic. I shall suggest a number of amendments to avoid these drawbacks and incorporate these amendments in a definition of a democratic collective choice. The result is a definition of a democratic decision that is superior to Tännsjö’s, or so we will argue. It will be acknowledged, however, that it still has some shortcomings that future efforts need to rectify, or that it might not be possible to give a 1 simple and naturalistic definition of democracy that is sufficiently in line with common language use. To avoid a possible misunderstanding, let me add that I don’t think there is necessarily one correct way of defining a democratic decision (although there might be a common kernel) and I certainly don’t suggest that the definition proposed here is the only way of explicating the ordinary language use of the term “democracy”.. (shrink)

Scientific inquiry is represented as a process of rational hypothesis revision in the face of data. For the concept of rationality, we rely on the theory of belief dynamics as developed in [5, 9]. Among other things, it is shown that if belief states are left unclosed under deductive logic then scientific theories can be expanded in a uniform, consistent fashion that allows inquiry to proceed by any method of hypothesis revision based on "kernel" contraction. In contrast, if belief (...) states are closed under logic, then no such expansion is possible. (shrink)

Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a (...) novel Cross-Dataset Feature Alignment and Generalization framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment and a dual aligned-to-generalized encoders model are employed to represent the feature. Then, a support vector machine is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods. (shrink)

In order to discuss the notion of presence, I explore Fascist Italy as an example of a presence-based culture. In the first part of this paper, I focus on the doctrines of "the philosopher of fascism," Giovanni Gentile , in order to show that his programme of cultural awakening revolves around the notion of the "presentification of the past." This notion formed the basis of Gentile's dialectic of the act of thought, which is the kernel of his actual idealism, (...) or actualism. I argue that actualism should primarily be interpreted as an ontology of a historical reality; it expresses the view that reality is history. In his 1914 inaugural "L'esperienza pura e la realtà storica" , Gentile drew this view to its ultimate consequence by developing a view of experience that has some striking parallels with the contemporary views of presence as expounded by Gumbrecht, Runia, and Ankermit. In the second part of my paper, I discuss how Gentile and his collaborators put presence into practice in school reforms, the Enciclopedia Italiana, and in hundreds of monuments, memorials, and exhibitions. Finally, I discuss the 1932 Mostra della rivoluzione fascista, which was not only the apex of Fascist culture politics, but also of the practice of presence. In this context, I argue that this practice should not be seen as a politics of historical interpretation, as Hayden White once held, but as a politics of sublime historical experience, or presence. The presence of presence in Fascist political culture raises some difficult questions for all who embrace the new paradigm, questions that can only be answered if the notion of presence is somehow balanced by the critical historical method, which is the basis for a true dialogue with the past. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reading and Writing PlatoCharles L. GriswoldThe Play of Character in Plato's Dialogues, by Ruby Blondell; 452 pp. Cambridge: Cambridge University Press, 2002, $55.00Plato's Dialectic at Play: Argument, Structure, and Myth in theSymposium, by Kevin Corrigan and Elena Glazov-Corrigan; 266 pp. University Park: Pennsylvania State University Press, 2004, $25.00Questioning Platonism: Continental Interpretations of Plato, by Drew Hyland; ix & 202 pp. Albany: State University of New York Press, 2004, $44.00The (...) People of Plato: A Prosopography of Plato and Other Socratics, by Debra Nails; 414 pp. Indianapolis: Hackett, 2002, $75.00.Why did Plato write dialogues? How should his dialogues be read (does the literary form matter, for example, to our getting at the heart of what Plato is saying)? These questions are now familiar in contemporary Plato scholarship, and their answers—which undoubtedly are interconnected—are the subject of lively and fascinating exploration. Any doubt that Platonic studies flourishes would quickly be put to rest by the four books under review here.It was once thought that attention to the "dramatic dimension" of Plato's literary-philosophical productions was the province of two much despised schools of thought: "the Straussians," and "the Continentals." The first of these was viewed as advocating a dangerous combination of esotericism (the idea that somehow Plato's meaning is not on the surface of the dialogues, but concealed in them, as though they were a cryptogram), elitism (the idea that the true Plato is only available to [End Page 205] "the few"—such as the Straussians, according to themselves anyhow), and unphilosophical and dilettantish meanderings about this or that literarily cast "teaching." Straussian hermeneutics was thought to open the way to unprincipled appeals to irony whenever it suited the interpreter, so that any text could end up saying pretty much whatever the interpreter wished for it to say, including the opposite of what the surface of the dialogue appeared to say. And further, it was thought (perhaps in a manner at odds with the "unprincipled interpretation" charge) that the Straussians' mode of "taking into account" the dramatic context—and in particular their contextualization of claims made in the dialogues such that this or that claim gets "explained" as a rhetorical gesture addressed by character X to character Y in context Z, a gesture whose content and validity, so to speak, are limited to that exchange—robs the dialogue of its philosophical content. Hence the worry that Straussian hermeneutics leads to skepticism about the possibility of philosophy itself—philosophy understood precisely in the way that it is regularly spoken of in the Platonic dialogues, namely as the effort to transcend perspective and partiality and to grasp through argument the essence of the matter as such. To cap it off, there is the background suspicion that Straussians are politically conservative, in a way that is somehow connected with their interpretation of Plato.There is more than a kernel of truth in the view that Straussians have insisted on the hermeneutic importance of the dialogue form, taking their cue from Strauss's own remarks in such texts as The City and Man. He did argue that Plato's meaning is not on the surface (and thus in some sense is esoteric), that it was intended by Plato to be available to "the few" while not debunking the beliefs of "the many," and that Plato is an ironist. There is also warrant for the worries about unprincipled interpretation, as well as about the reduction of philosophy to a sort of literary sensibility substituting for genuine philosophical argument (where, after all, are Strauss's arguments for any philosophical position, whether or not about a Platonic "teaching"?). And it is by and large true, so far as I can tell, that his self-identified followers are politically conservative (in the contemporary American sense), and in a way somehow tied to their interpretation of Plato.I myself find Strauss's interpretation of Plato to be deeply problematic both in its substance and its method (I put aside the issue of the political conservatism of Strauss's followers, a "teaching" I also find objectionable). And while I join the authors of the four... (shrink)

The interviewer's desire for rapport with the respondent is both the greatest weakness and the greatest strength of semi-structured interviewing. As has been discussed at some length, structured interviews present difficulties with aged or mentally ill respondents who are unwilling or unable to play the game involved therein. Structured interviews also are impregnated with subjectivity in the form of working assumptions made by the researcher. For these reasons, they are likely to yield little understanding of the experiential world of the (...) elderly. It has been suggested here that the major strength of the semi-structured interview is its flexibility, allowing the researcher to grasp more of the substance of the old person's world than is permitted in a more rigid methodological technique. This is not to say that the entire substance of the older person's world can ever be touched; one's understanding of anyone else will be imperfect. As Laing observed about the schizophrenic: What is required of us? Understand him? The kernel of a schizophrenic's experience of himself must remain incomprehensible to us.... But comprehension as an effort to reach and grasp him by our own categories whereby he inevitably falls short is not what the schizophrenic wants or requires. We have to recognize all the time his distinctiveness and differentness, his separateness and loneliness and despair.16 Semi-structured interviews such as those just described attempt to take into consideration both the nature of the experience of the interviewer and the respondent and their distinctiveness. Through this method, one can hope not only to describe particular experiences and perceptions of the respondent but also to "set them within the gestalt." With such information, perhaps one can not only move closer to a phenomenology of the interview but also possess more reliable tools in social science that will be of use with a variety of populations. Hopefully, one may also glean knowledge about the aging process, in both its normal and pathological aspects, that will provide a more complete understanding about this last stage of the life cycle and its relationship to all of life. (shrink)

At first glance, it may seem that climate policy based on estimates of the social cost of carbon (SCC) presupposes a set of controversial assumptions, especially about what detailed knowledge regulators have about the impacts of climate change, and what the proper role of government and policy is in responding to those impacts. However, I explain why the SCC-based approach need not actually have these problematic presuppositions as well as why SCC estimates may provide the best guide to climate policy (...) when implemented in a way that incorporates a healthy dose of humility. The SCC-based approach can be used in a way that is ecumenical between the wide range of reasonable but incompatible views about the proper goals of government and policy, ranging from views that aim only at market-efficiency, to utilitarian views, to rights-based and other deontological views, to libertarian views, to virtue ethics views, and others. Beyond this, I suggest that the SCC-based approach can help us find an overlapping consensus on a particular climate policy given the range of reasonable but incompatible normative views endorsed in diverse societies. Finally, I consider a number of specific objections to the SCC-based approach that are especially prominent in contemporary discourse and policy debates. I suggest that even if we agree that all of these objections to existing SCC-based analyses have an important kernel of truth, nonetheless the correct response to climate change still involves substantial emissions reductions to be achieved via policy, and the best methods for deciding the magnitude of those reductions still depend essentially on the SCC-based approach. (shrink)

Marginal structural models can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting can be sensitive to model misspecification and lead to high-variance estimates due to extreme weights. Various methods have been proposed to partially address this, including covariate balancing propensity score to mitigate treatment model misspecification, and truncation and stabilized-IPTW to temper extreme weights. In this article, (...) we present kernel optimal weighting, a convex-optimization-based approach that finds weights for fitting the MSMs that flexibly balance time-dependent confounders while simultaneously penalizing extreme weights, directly addressing the above limitations. We further extend KOW to control for informative censoring. We evaluate the performance of KOW in a simulation study, comparing it with IPTW, sIPTW, and CBPS. We demonstrate the use of KOW in studying the effect of treatment initiation on time-to-death among people living with human immunodeficiency virus and the effect of negative advertising on elections in the United States. (shrink)

The well-known classifier support vector machine has many parameters associated with its various kernel functions. The radial basis function kernel, being the most preferred kernel, has two parameters to be optimized. The problem of optimizing these parameter values is called model selection in the literature, and its results strongly influence the performance of the classifier. Another factor that affects the classification performance of a classifier is the feature subset. Both these factors are interdependent and must be dealt (...) with simultaneously. Following the multiobjective definition of feature selection, we have applied a multiobjective genetic algorithm, NSGA II, to optimize the feature subset and model parameters simultaneously. Comparison of the proposed approach with the grid algorithm and GA-based method suggests that the MOGA-based approach performs better than the grid algorithm and is as good as the GA-based approach. Moreover, it provides multiple solutions instead of a single solution. The users can prefer one feature subset over the other as per their requirement and available resources. (shrink)

This thesis consists in six articles and a comprehensive summary. • The pourpose of the summary is to introduce the AGM theory of belief change and to exemplify the diversity and significance of the research that has been inspired by the AGM article in the last 25 years. The research areas associated with AGM was divided in three parts: criticisms, where we discussed some of the more common criticisms of AGM. Extensions where the most common extensions and variations of AGM (...) are presented and applications where we provided an overview of applications and connections with other areas of research. • Article I elaborates on the connection between partial meet contractions [AGM85] and kernel contractions [Han94a] in belief change theory. Also both functions are equivalent in belief sets, there are notequivalent in belief bases. A way to define incision functions from selection functions and vice versa is presented. It is explained under which conditions there are exact correspondences between selection and incision functions so that the same contraction operations can be obtained by using either of them. • Article II proposes an axiomatic characterization for ensconcement-based contraction functions, belief base functions proposed byWilliams and relates this function with other kinds of base contraction functions. • Article III adapts the Fermé and Hansson model of Shielded Contraction [FH01] as well as Hansson et all Credibility-Limited Revision [HFCF01] for belief bases, to join two of the many variations of the AGM model [AGM85], i.e. those in which knowledge is represented through belief bases instead of logic theories, and those in which the object of the epistemic change does not get the priority over the existing information as it is the case in the AGM model. • Article IV introduces revision by comparison a refined method for changing beliefs by specifying constraints on the relative plausibility of propositions. Like the earlier belief revision models, the method proposed is a qualitative one, in the sense that no numbers are needed in order to specify the posterior plausibility of the new information. The method uses reference beliefs in order to determine the degree of entrenchment of the newly accepted piece of information. Two kinds of semantics for this idea are proposed and a logical characterization of the new model is given. • Article V focuses on the extension of AGM that allows change for a belief base by a set of sentences instead of a single sentence. In [FH94], Fuhrmann and Hansson presented an axiomatic for Multiple Contraction and a construction based on the AGM Partial Meet Contraction. This essay proposes for their model another way to construct functions: Multiple Kernel Contraction, that is a modification of Kernel Contraction,proposed by Hansson [Han94a] to construct classical AGM contractions and belief base contractions. • Article VI relates AGM model with the DFT model proposed by Carlos Alchourrón [Alc93]. Alchourrón devoted his last years to the analysis of the notion of defeasible conditionalization. His definition of the defeasible conditional is given in terms of strict implication operator and a modal operator f which is interpreted as a revision function at the language level. This essay points out that this underlying revision function is more general than AGM revision. In addition, a complete characterization of that more general kind of revision that permits to unify models of revision given by other authors is given. (shrink)

African literature has played a major role in changing and shaping perceptions about African people and their way of life for the longest time. Unlike western cultures that are associated with advanced forms of writing, African literature is oral in nature, meaning it has to be recited and even performed. Although Africa has an old tribal culture, African philosophy is a new and strange idea among us. Although the problem of “universality” of African philosophy actually refers to the question of (...) whether Africa has heckling of philosophy in the Western sense, obviously, the philosophy bred by Africa’s native culture must be acknowledged. Therefore, the human–computer interaction-oriented method is proposed to appreciate African literature and African philosophy. To begin with, a physical object of tablet-aid is designed, and a depth camera is used to track the user’s hand and tablet-aid and then map them to the virtual scene, respectively. Then, a tactile redirection method is proposed to meet the user’s requirement of tactile consistency in head-mounted display virtual reality environment. Finally, electroencephalogram emotion recognition, based on multiscale convolution kernel convolutional neural networks, is proposed to appreciate the reflection of African philosophy in African literature. The experimental results show that the proposed method has a strong immersion and a good interactive experience in navigation, selection, and manipulation. The proposed HCI method is not only easy to use, but also improves the interaction efficiency and accuracy during appreciation. In addition, the simulation of EEG emotion recognition reveals that the accuracy of emotion classification in 33-channel is 90.63%, almost close to the accuracy of the whole channel, and the proposed algorithm outperforms three baselines with respect to classification accuracy. (shrink)