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Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.

T. S. ELIOT often showed a preoccupation with childhood, and at times this preoccupation showed itself as a search for a lost Eden. In Burnt Norton the leaves are alive with the laughter of children:

New approaches for non‐classical neural‐based computing are introduced. The developed approaches utilize new concepts in three‐dimensionality, invertibility and reversibility to perform the required neural computing. The various implementations of the new neural circuits using the introduced paradigms and architectures are presented, several applications are shown, and the extension for the utilization in neural‐systolic computing is also introduced.

The new neural paradigms utilize new findings in computational intelligence and advanced logic synthesis to perform the functionality of the basic neural network (NN). This includes the techniques of three‐dimensionality, invertibility and reversibility. The extension of implementation to neural‐systolic computing using the introduced reversible neural‐systolic architecture is also presented.

Novel NN paradigms are introduced in this paper. New 3D paradigm of NL circuits called three‐dimensional inverted neural logic (3DINL) circuits is introduced. The new 3D architecture inverts the inputs and weights in the standard neural architecture: inputs become bases on internal interconnects, and weights become leaves of the network. New reversible neural network (RevNN) architecture is also introduced, and a RevNN paradigm using supervised learning is presented. The applications of RevNN to multiple‐output feedforward discrete plant control and to reversible neural‐systolic computing are also shown. Reversible neural paradigm that includes reversible neural architecture utilizing the extended mapping technique with an application to the reversible solution of the maze problem using the reversible counterpropagation NN is introduced, and new neural paradigm of reversibility in both architecture and training using reversibility in independent component analysis is also presented.

Since the new 3D NNs can be useful as a possible optimal design choice for compacting a learning (trainable) circuit in 3D space, and because reversibility is essential in the minimal‐power computing as the reduction of power consumption is a main requirement for the circuit synthesis of several emerging technologies, the introduced methods for non‐classical neural computation are new and interesting for the design of several future technologies that require optimal design specifications such as three‐dimensionality, regularity, super‐high speed, minimum power consumption and minimum size such as in low‐power control, adiabatic signal processing, quantum computing, and nanotechnology.

Starting with a Moore‐type automaton the bases for a brain‐like sequential machine are laid down. The problem is considered both at the level of a physical structure and a state structure. The logic is cellular and variable to accommodate learning and generalization. It is shown that this structure can “learn to live” in a consistent environment. Concepts such as recognition and recall of environmental events, short‐term memory, data generation (analogous to speech production) and attention are shown to be natural attributes of the model.

This analysis is the first to explore the overall roles of the offsetting attraction and distraction influences of earnings news in shaping the level of attention given to the equity market by market participants.

We use multivariate regression approach and examine how trading activity levels within the set of non-announcing firms varies with respect to collective measures of contemporaneous earnings announcement visibility. We employ attention and information transfer theories in our hypothesis development.

This analysis is the first to explore the overall roles of the offsetting attraction and distraction influences of earnings news in shaping the level of attention given to the equity market by market participants. Specifically, we examine how the number of earnings announcement activity affects investor attention as measured by trading volume given to the set of non-announcing firms. We find that while earnings announcement numbers lower trading volume responses to earnings news among announcing firms (consistent with Hirshleifer et al., 2009), their distractive influence does not carry over into the market as a whole. More importantly, investor attention to both the overall market and the larger subset of non-announcing firms increase in response to earnings news activity levels. However, after decomposing the announcers as same-industry and different-industry announcers, we find that investor attention to the non-announcing segment of the market increases with the number of same-industry announcers, but actually seems to decrease (i.e. they distract attention) with the number of different-industry announcers. We also find that the associated earnings surprise brings attention to non-announcing firms (consistent with earnings news is relevant to overall market price movements). Finally, we find that distraction effects are attenuated in the financial crisis period.

A promising area of future research is to examine the relation between market pricing efficiency and aggregate earnings activity for the set of non-announcing firms. Although it will be a challenging task to measure pricing efficiency for the non-announcers, this will complement the prior literature only focusing on the announcing segment of the market.

First, instead of assessing the impact of number of earnings announcements on the subset of announcing firms, which is a micro-level perspective, we identify the impact of news arrivals on all firms in the market including the vastly larger set of non-announcing firms. Second, by decomposing the number of announcements into industry-related and -unrelated news we show that different types of news arrivals spark investor attention differently, suggesting the importance of categorizing the news into related and unrelated industries.

A potential future area of research identified by our analysis is to investigate what type of investors' attention is distracted or attracted during the earnings announcements. A promising area of future research is to examine the relation between market pricing efficiency and aggregate earnings activity for the set of non-announcing firms.

This paper is the first one exploring the overall roles of the offsetting attraction and distraction influences of earnings announcements in shaping the level of investor attention given to the equity market by market participants. Our findings should be of interest to investors, analysts, security market regulators and researchers.

The consumption of alcoholic beverages has been a social custom throughout the world since the beginning of recorded history. Various wines, beers, and liquors have been a part of almost every culture since ancient times. The modern cocktail originated in Elizabethan England and quickly spread throughout the world. And just as new experiments continually add to the variety of known alcoholic beverages, new books appear describing these drinks and the recipes required to mix them. From yesterday's mulled wine and cider to today's Mai Tai, Margarita, and Tequila Sunrise, bartenders and home party planners refer to these sources when making both traditional and exotic drink recipes. This review is a comparison of thirteen currently available commercial bar guides. While libraries have not traditionally collected in the bar guide genre, every library should have at least one in its collection for use as a reference source or for patrons to use in their homes.

This chapter shows that Mead has a field theory and that the explanatory method of symbolic interaction is that of a field. A field, in this sense, is a systematic network of meanings. When someone or something enters that field such as a protest rally or a cocktail party they are given the meaning that is characteristic of the field. This explanation is not one of causation but one of context. I show that a major field theory of Mead’s concerns the agent and how decisions or actions are made. He also has a developmental field theory based on the play-game-generalized other relation. With Mead’s agency model I then show how it can be applied, in macro fashion, to the recent rise in American minorities, especially that of women, African Americans, and gays. This example shows the macro or social structural power of Mead’s idea.

The following are portions of a paper, bearing the title as above, which was read before the Royal Society of Arts on April 18th, 1945, by Sir Edward V. Appleton, LL.D., F.R.S., the Secretary of the Department; Sir Henry Dale, P.R.S., presiding.