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Intermittent transition to chaos in vibroimpact system

this information too, such as short time Fourier transform, Wigner distributions, etc.). Wavelet transform is capable of providing the time and frequency information simultaneously, hence giving a time-frequency representation of the signal. The WT was developed as an alternative to the short time Fourier Transform (STFT). Like the FT the continuous wavelet transform (CWT) uses inner products to measure the similarity between a signal and an analyzing function. In the FT the analyzing functions are the complex exponents e jωt . The resulting transform is a

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Five Years of Phase Space Dynamics of the Standard & Poor’s 500

and roughness of the stock, and the market state corresponds to a certain distribution of stock in phase space. The novelty of our approach is twofold. First, in Sec. 4.1 , we have developed the novel concept of predictability of states in phase space. Our approach is based on the idea that with some probability a state might be a t -day precursor of another state in phase space. The introduced measure of t -day predictability of a state is a sum of all information components from its t-day precursors. From such a point of view, Markov chains are

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Petabytes in Practice: Working with Collections as Data at Scale

those which would be available with manual labels alone. Convolutional Neural Networks (CNNs) ( LeCun, Kavukcuoglu, and Farabet, 2010 ) are modeled after mammalian vision, where invariant features of the visual environment that mark a significant event (such as the movement of a predator or prey across the field of vision) are recognized amid the range of incoming stimuli. Its function is to take a first pass at extracting features. It is attempting to recognize characters of text by recognizing the invariant features characteristic of letters in the words. Long

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Labor Migration in Indonesia and the Health of Children Left Behind

studies on the relationship between child health and socioeconomic outcomes, this study uses anthropometric measures of child health rather than subjective health status. Examples of papers in development studies that employ anthropometric measures of health are Domingues and Barre (2013) for Mozambique, and Brainerd (2010) for the Soviet Union. Also, the longitudinal design of the IFLS allows for the elimination of all unobserved child- and household-level time-invariant characteristics that are correlated with the explanatory variables, removing a major source of

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Inequality in Posting Behaviour Over Time
A study of Danish Facebook users

media networks is very imprecise. The overall framework is digital participation as creative content, and it is unclear exactly what that includes on social networks. Hargittai (2007) also suggested time spent on the internet as a predictor of participation, indirectly referring to the longitudinal perspective. The article thus builds on the assumption that the study will find an overall increase in posting during the period studied (Hypothesis 1). Previous studies have shown that education is an important variable to consider when measuring inequality online

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Reallocation and the Role of Firm Composition Effects on Aggregate Wage Dynamics

productivity at the two-digit sector level and with a measure of competition (Herfindahl index). This evidence is indirect and only suggestive, and we leave to future research a full test of our hypothesis and an exploration of its implications. The paper proceeds as follows. After describing the data in section 2, we replicate composition studies by employing a standard tool in labor economics to assess differences among groups of workers, the BO decomposition, which we augment with employers’ characteristics – section 3. We proceed by applying on wage data a standard

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Regional dimension of firm level productivity determinants: the case of manufacturing and service firms in Ukraine

invariant to the intensity of use of observable factor inputs is often employed in the productivity measurement. This measure is called total factor productivity (TFP). The difference in TFP reflects variation in output produced from a fixed set of inputs. Firm with higher-TFP produce greater amounts of output with the same set of observable inputs than firms with lower-TFP. TFP is most easily seen in the formulation of a production function where output is the product of a function of observable inputs and a factor-neutral shifter. This means that TFP is a residual. Over

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Drive for Muscularity and Disordered Eating Behavior in Males: the Mediating Role of Cognitive Appraisal

practised their activity from one to four sessions per week ( M = 2.20; SD = 0.91). Participants from sport clubs had a mean age of 26.18 years ( SD = 6.61; minimum = 18; maximum = 47) and practised their activity from one to four sessions per week ( M = 2.58; SD = 0.89). Measures Drive for Muscularity Scale (DMS; McCreary and Sasse, 2000 ). This instrument evaluates muscularity-oriented attitudes and behaviors, including two dimensions: (a) muscularity-oriented body image (seven items; α = 0.91 for this study), and (b) muscularity-oriented behaviors

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Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data

1 Introduction The data Data is are quantizsed symbol of the information. Data clustering is a process to find the effective information and hidden structure feature based on data collection and reasonable division by a similarity measure, which is an important data mining technique for unsupervised learning and have an is important and widely used in pattern recognition [ 1 , 2 , 3 ], machine learning [ 4 , 5 ], image processing [ 6 , 7 ] and other fields. In the era of Big Data, a great deal of valuable data information is produced at all times with the

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Some Invariants of Flower Graph

+\frac{5}{3}n$$ Augmented Zagreb Index A ( f ( n×m ) ) S x 3 Q ( n − 2 ) J D x 3 D y 3 f ( x , y ) | x = 1 = 2 3 m n + ( 2 5 − 2 3 .3 + 2.3 ( − 3 ) .4 4 ) n $$S_{x}^{3}Q_{(n-2)}JD_{x}^{3}D_{y}^{3}f(x,y)|_{x=1} \\ =2^{3}mn+(2^{5}-2^{3}.3+2.3^{(-3)}.4^{4})n$$ Measure of Irregularity I R M ( f ( x , y ) ) = 8 n

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