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)

Kernel contraction is a natural nonrelational generalization of safe contraction. All partial meet contractions are kernel contractions, but the converse relationship does not hold. Kernel contraction is axiomatically characterized. It is shown to be better suited than partial meet contraction for formal treatments of iterated belief change.

The existence claim of a "genetic program" encoded in the DNA molecule which controls biological processes such as development has been examined. Sources of belief in such an entity are found in the rhetoric of Mendelian genetics, in the informationist speculations of Schrodinger and Delbruck, and in the instrumental efficacy found in the use of certain viral, and molecular genetic techniques. In examining specific research models, it is found that attempts at tracking the source of biological control always leads back (...) to the complex state of a cell as a whole, the organizational structure of which is not itself encoded in the DNA. It is argued that the "genetic program" is not an existing biological entity and its philosophical and heuristic status is challenged. (shrink)

This paper 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. We propose for their model another way to construct functions: Multiple Kernel Contraction, that is a modification of Kernel Contraction, proposed by Hansson [Han94] to construct classical AGM contractions and belief base contractions. (...) This construction works out the unsolved problem pointed out by Hansson in [Han99, pp. 369]. (shrink)

This paper 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. We propose for their model another way to construct functions: Multiple Kernel Contraction, that is a modification of Kernel Contraction, proposed by Hansson [Han94] to construct classical AGM contractions and belief base contractions. (...) This construction works out the unsolved problem pointed out by Hansson in [Han99, pp. 369]. (shrink)

We discuss issues related to constructing an orthomodular structure from an object in a category. In particular, we consider axiomatics related to Baer *-semigroups, partial semigroups, and various constructions involving dagger categories, kernels, and biproducts.

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)

I propose the Shared Presupposition Norm of Joking (SPNJ) as a constitutive norm of joke-telling. This norm suggests that a person should only tell a joke if they believe their audience shares the presuppositions—both explicit beliefs and implicit inferential connections—upon which the joke turns. Without this shared understanding, the audience would lack the necessary comprehension to appreciate the joke. I defend this norm in an analogous way to Williamson’s defense of the Knowledge Norm of Assertion by demonstrating that it explains (...) a number of patterns of joking behavior. If SPNJ is indeed a constitutive norm of joking, it provides an epistemological foothold. Jokes function as conversational explicatures serving an epistemically diagnostic function, revealing implicit and perhaps disavowed inferential dispositions of the joking community. This norm does not teach us about the subject matter of the joke, but instead illuminates the language games of the joke teller and audience. (shrink)

We have been recently engaged in this search, and have announced a long series of successively simpler and more convincing examples. See [Fr09-10].

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)

In the framework of transferable utility games, we modify the 2-person Davis–Maschler reduced game to ensure non-emptiness of the imputation set of the adapted 2-person reduced game. Based on the modification, we propose two new axioms: reduced game monotonicity and reduced dominance. Using RGM, RD, NE, Covariance under strategic equivalence, Equal treatment property and Pareto optimality, we are able to characterize the kernel.

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)

Barut's classicalzitterbewegung model includes the internal dynamical variables and the quantization of this system gives a general transition amplitude between the different space-time points and internal coordinates and momentum. It includes the transition amplitude between the half integer and integer spin eigenvalues. Spin eigenfunctions lead to all sets of relativistic wave equations, as well as the Dirac equation.

In this note we shall show that if L is a balanced pseudocomplemented Ockham algebra then the set ${\fancyscript{I}_{k}(L)}$ of kernel ideals of L is a Heyting lattice that is isomorphic to the lattice of congruences on B(L) where ${B(L) = \{x^* | x \in L\}}$ . In particular, we show that ${\fancyscript{I}_{k}(L)}$ is boolean if and only if B(L) is finite, if and only if every kernel ideal of L is principal.

Two of the most well-known belief contraction operators are partial meet contractions (PMCs) and kernel contractions (KCs). In this paper we propose two new classes of contraction operators, namely the class of generalized partial meet contractions (GPMC) and the class of generalized kernel contractions (GKC), which strictly contain the classes of PMCs and of KCs, respectively. We identify some extra conditions that can be added to the definitions of GPMCs and of GKCs, which give rise to some interesting (...) subclasses of those classes of functions, namely the classes of extensional and of uniform GPMCs/GKCs. In the context of contractions on belief sets the classes of partial meet contractions, uniform GPMCs and extensional GPMCs are all identical. Nevertheless, when considered as operations on belief bases, the class of uniform GPMCs coincides with the class of partial meet contractions, but the extensional GPMCs constitute a new kind of belief base contraction functions whose characterizing postulate of irrelevance of syntax is extensionality—the same postulate of irrelevance of syntax which occurs in the classical axiomatic characterization of partial meet contractions for belief sets—rather than the postulate of uniformity—which is the irrelevance of syntax postulate used in the axiomatic characterization for partial meet contractions on belief bases. Analogous results are obtained regarding the classes of extensional and of uniform GKCs. We present the interrelations in the sense of inclusion among all the new classes of operators presented in this paper and several well known classes of PMCs and of KCs. (shrink)

In this note, it is shown that the set of kernel ideals of a K n, 0-algebra L is a complete Heyting algebra, and the largest congruence on L such that the given kernel ideal as its congruence class is derived and finally, the necessary and sufficient conditions that such a congruence is pro-boolean are given.

We are concerned with the following problem. Suppose Γ and Σ are closed permutation groups on infinite sets C and W and ρ: Γ → Σ is a non-split, continuous epimorphism with finite kernel. Describe the possibilities for ρ. Here, we consider the case where ρ arises from a finite cover π: C → W. We give reasonably general conditions on the permutation structure W;Σ which allow us to prove that these covers arise in two possible ways. The first (...) way, reminiscent of covers of topological spaces, is as a covering of some Σ-invariant digraph on W. The second construction is less easy to describe, but produces the most familiar of these types of covers: a vector space covering its projective space. (shrink)

It is often believed that the only alternative to an idealist conception of natural phenomena excludes both the presence of objective universal forms and their progression towards higher forms as the finality of processes in the natural world. Realism regarding the universal and teleological approaches regarding processes are signs of idealism. Therefore, materialism, it would seem, must conform to a nominalist and mechanical view of nature. However, an intelligent materialist reading of idealism’s classics reveals a more complex scenario. A real (...) fact is expressed in a mystical fashion in idealism’s conceptions of objective universals and teleology. This article attempts to show such a real fact in its authentic (materialist) form. With that goal in mind, the present article discusses the notion of nature’s alienation, the distinction between abstract and concrete universals, and the concept of dialectical interaction. The natural-historical emergence of a higher form of matter’s organization assimilates, as its organs, the conditions that preceded it in time, transforming itself into an active producer of such conditions. That is the secret glimpsed but not correctly understood by idealism that a consistent materialist should not ignore. (shrink)

An endomorphism on an algebra \ is said to be strong if it is compatible with every congruence on \; and \ is said to have the strong endomorphism kernel property if every congruence on \, other than the universal congruence, is the kernel of a strong endomorphism on \. Here we characterise the structure of those double MS-algebras that have this property by the way of Priestley duality.

We now fix A ⊆ Q. We study a fundamental class of digraphs associated with A, which we call the A-digraphs. An A,kdigraph is a digraph (Ak,E), where E is an order invariant subset of A2k in the following sense. For all x,y ∈ A2k, if x,y have the same order type then x ∈ E ↔ y ∈ E.

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)

In his book, The Case Against Perfection, Michael J. Sandel has offered several arguments against biomedical human enhancements. However, his views have been forcefully criticized by Frances M. Kamm. This paper argues that while Kamm is correct in arguing that Sandel fails to establish the moral impermissibility of enhancements, he, nevertheless, offers resources for articulating our unease with enhancements. In particular, this paper argues that being willing to enhance oneself in any way is incompatible with having an identity as a (...) person. (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)

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)

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)