Equity Crowdfunders’ Human Capital and Signal Set Formation: Evidence From Eye Tracking

Signaling theory typically assumes that attention is always given to observable signals. We study signal receivers’ formation of signal sets—the signals to which receivers attend and that they can use for subsequent interpretations. Drawing on a cognitive perspective, we argue that signal receivers’ human capital influences the volume and type of signals they attend to and the time they take to form signal sets. Using eye tracking, we show that equity crowdfunders do not attend to many signals that are easily observable on a campaign page, and that differences in crowdfunders’ human capital uniquely affect their signal set formation.

THE INFLUENCE OF BIASED SIGNAL SET ON STOCK MARKET—BASED ON THE SHANGHAI COMPOSITE INDEX

Based on the two phenomena of over and underreaction, this paper introduces the characteristics of signal set into the model, which studies the price deviation of financial market caused by investors' signal set deviation. The text concludes that investors overreact to information of low weight and underreact to information of high weight, and uses cross-section analysis and time series analysis to verify the correctness of the results.

Behavioral Modeling of Memristor-Based Rectifier Bridge

In electrical engineering, radio engineering, robotics, computing, control systems, etc., a lot of nonlinear devices are synthesized on the basis of a nanoelement named memristor that possesses a number of useful properties, such as passivity, nonlinearity, high variability of parameters, nonvolatility, compactness. The efficiency of this electric element has led to the emergence of many memristor technologies based on different physical principles and, as a result, to the occurrence of different mathematical models describing these principles. A general approach to the modeling of memristive devices is represented. The essence is to construct a behavioral model that approximates nonlinear mapping of the input signal set into the output signal set. The polynomials of split signals, which are adaptive to the class of input signals, are used. This adaptation leads to the model’s simplification important in practice. Multi-dimensional polynomials of split signals are built for the rectifier bridge at harmonic input signals. The modeling error is estimated in the mean-square norm. It is shown that the accuracy of the modeling is increased in the case of using the piecewise polynomial with split signals.

The edge signal number of a graph

For two vertices [Formula: see text] and [Formula: see text] in a connected graph [Formula: see text], the signal distance [Formula: see text] from [Formula: see text] to [Formula: see text] is defined by [Formula: see text], where [Formula: see text] is a path connecting [Formula: see text] and [Formula: see text], [Formula: see text] is the length of the path [Formula: see text] and in the sum [Formula: see text] runs over all the internal vertices between [Formula: see text] and [Formula: see text] in the path [Formula: see text]. A path between the vertices [Formula: see text] and [Formula: see text] of length [Formula: see text] is called a [Formula: see text] geosig path. A set [Formula: see text] is called a signal set, if every vertex [Formula: see text] in [Formula: see text] lies on a geosig path joining a pair of vertices of [Formula: see text]. The signal number [Formula: see text] is the minimum order of a signal set of a graph [Formula: see text]. An edge signal cover of [Formula: see text] is a set [Formula: see text] such that every edge of [Formula: see text] is contained in a geosig path joining some pair of vertices of [Formula: see text]. The edge signal number [Formula: see text] of [Formula: see text] is the minimum order of an edge signal cover and any edge signal cover of order [Formula: see text] is an edge signal basis of [Formula: see text]. In this paper, we initiate a study on the edge signal number of a graph [Formula: see text].

Remarks on Traffic Signal Coordination

The co-ordination between traffic signals is assumed to be important for the good organization of a transport system. By using an artificial approach to create and analyze a multitude of transportation systems, a few different simple traffic signals programs has been put to the test and compared to each other. The result is that a well co-ordinated system can be outperformed by a non-coordinated signal set-up, where all signals controlers run in (single intersection) actuated mode. Clearly, these results are preliminary and require more investigation.