Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.     

Character-Principlism and the Particularity Objection

This paper is a response to particularist critics of the normative force of moral principles. The particularist critique, as I understand it, is a rejection not only of principle-based accounts of moral deliberation and justification, but also of accounts of character in which principles play a central role. I focus on the latter challenge and counter it with a view I call character-principlism .
I begin by discussing in a general way what motivates the particularity objection to principles and then contrast two views – both of which insist on the importance of attentiveness to particularity – about the relative normative status of principles and particular cases. I present some reasons for believing that we need a more normatively robust conception of the role of moral principles than the particularists provide. In the main portion of the paper, I discuss how character-principlism sees principles functioning in our lives and the lives we lead with others. I contrast this with some other accounts of desirable character that particularists can embrace, and argue that these are seriously flawed because, unlike character-principlism, they cannot satisfactorily explain how a person could possess the constancy of character that moral integrity requires.     

基于语义网络的创造性认知机制研究现状与展望

语义网络是主要的知识表征方式之一,对创造性研究具有重要意义。近年来不断发展的网络科学方法为相关研究提供了强大的方法论框架及量化工具,研究者得以分别从语义网络结构及语义相关认知操作的角度探讨语义网络与创造性的内在联系。已有证据表明语义网络中所储存的概念越丰富、概念间的连接通路越多元化或越灵活,则个体创造性水平越高;而个体创造性水平亦受制于个体对概念的检索、抑制、选择、整合及扩展等认知能力。在此方向上,未来研究工作应当更多地思考如何在一个相对整合的认知框架中深入探讨语义网络结构及相关认知过程对创造性的交互作用,并结合神经影像等技术手段,将语义网络之于创造性的认知机制嵌套于神经科学体系中进行解释。     

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia.     

Finding MAPs using strongly equivalent high order recurrent symmetric connectionist networks

Belief revision is the problem of finding the most plausible explanation for an observed set of evidences. It has many applications in various scientific domains like natural language understanding, medical diagnosis and computational biology. Bayesian Networks (BN) is an important probabilistic graphical formalism widely used for belief revision tasks. In BN, belief revision can be achieved by finding the maximum a posteriori (MAP) assignment. Finding MAP is an NP-Hard problem. In previous work, we showed how to find the MAP assignment in BN using High Order Recurrent Neural Networks (HORN) through an intermediate representation of Cost-Based Abduction. This method eliminates the need to explicitly construct the energy function in two steps, objective and constraints. This paper builds on that previous work by providing the theoretical foundation and proving that the resultant HORN used to find MAP is strongly equivalent to the original BN it tries to solve.     

Detecting Clusters/Communities in Social Networks

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.     

Attention network test: Assessment of cognitive function in chronic fatigue syndrome

Information processing difficulties are common in patients with chronic fatigue syndrome (CFS). It has been shown that the time it takes to process a complex cognitive task, rather than error rate, may be the critical variable underlying CFS patients' cognitive complaints. The Attention Network Task (ANT) developed by Fan and colleagues may be of clinical utility to assess cognitive function in CFS, because it allows for simultaneous assessment of mental response speed, also called information processing speed, and error rate under three conditions challenging the attention system. Comparison of data from two groups of CFS patients (those with and without comorbid major depressive disorder; n = 19 and 22, respectively) to controls (n = 29) consistently showed that error rates did not differ among groups across conditions, but speed of information processing did. Processing time was prolonged in both CFS groups and most significantly affected in response to the most complex task conditions. For simpler tasks, processing time was only prolonged in CFS participants with depression. The data suggest that the ANT may be a task that could be used clinically to assess information processing deficits in individuals with CFS.     

Lost in Translation? The Need for ‘Upstream Engagement’ with Nanotechnology on Trial

Nowadays, the criticism of the so-called ‘deficit model’ and the need for ‘upstream engagement’ in science and technology are becoming part of the master narratives of public policies in many countries, especially concerning nanotechnology. This may be considered as a major success for STS scholars, whose research results have largely contributed to this change, especially those concerning the GMO controversies. Some STS scholars thus move from a position of distant and critical observers to the role of experts in social engineering or advisers of policy-makers. However, in their enthusiasm concerning the expected benefits of upstream engagement, institutions, TA practitioners and social scientists seem to ignore some important limitations as well as the implicit framing assumptions of the concept. Based on an experience made by a group of social scientists in the Grenoble area—one of the major ‘nanodistricts’ in Europe—our paper shows that the ‘upstream engagement’ concept is still embedded in a linear model of innovation and is not very useful to anyone pursuing the co-production of innovations. It is especially true when socio-technical networks are already aligned by powerful actors and a worldwide agenda as in the case of nanotechnology. In order to give an opportunity for public engagement to have a larger impact on decision-making, we propose an alternative approach, which combines Actor–Network Theory (ANT), as an analytical tool, with the reflexive and ongoing implementation of public participation. Public engagement is probably one of the critical loci where STS scholars must reflect on the articulation between the knowledge they produce and public policies in action.     

Re‐framing systemic paradigms for the art of learning

Thoughtful people are increasingly concerned that the current paradigms for social, corporate, and educational activities are in disgraceful disarray. The “problem‐solving” or analytical model, the competitive or game model, the commercial or consumer model, the bureaucratic or institutional model, and the disease or illness model which prevail in public discourse are proving to be especially unwholesome. We cannot, however, educate ourselves without paradigms. A credible educational paradigm must be generally accessible without being simplistic, informative without being monothematic, and accommodating as well as discriminating. Given our disquiet with the current cognitive situation, a renewing paradigm must be somehow novel; given the character of human nature, a sustaining paradigm must be somehow familiar.

For a very long time now, professional Sciences have committed themselves to paradigms about “reality out there,” while professional Arts have devoted themselves to expressing “imagination from within here.” The more these two worldviews polarize in opposition to one another, the more room there is—and the more human heed there becomes—for mediation by an applied philosophy which accommodates the “real” as well as the “imaginary” in a complementary way. Such a philosophy would address not only “what do you know?” and “how do you do?” but also “how do you know?” and “why do you do?” In earlier times, people would have been considered neither educated nor wise unless they appreciated the Sciences and the Arts whole. In our time, we may not survive unless we can re‐integrate our fractured perceptions. How might we proceed to do so? There may be a systemological way.     

Deconstructing the construct: A network perspective on psychological phenomena

In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like ’depression’ is not seen as a latent variable that underlies symptoms like ’lack of sleep’ or ’fatigue’, and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep → fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real.