Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.     

Evolution and Collision of Bose-Condensed Gases in an Infinitely Deep Square Well

The evolution and collision of Bose-condensed gases in one-dimensional optical lattices are investigated in the presence of an infinitely deep square well created by two far-off resonant laser beams.The two far-off resonant laser beams are superimposed on the combined potential consisting of a magnetic trap and one-dimensional optical lattices.After the combined potential is switched off,the analytical result of the evolution of the density distribution of the Bose-condensed gas is given by using the propagator method.The collisions between the condensate and the infinitely deep square well are shown in the present work.     

What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins’ price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.     

Why Does the Geometric Product Simplify the Equations of Physics?

In the last decades it was observed that Clifford algebras and geometric product provide a model for different physical phenomena. We propose an explanation of this observation based on the theory of bounded symmetric domains and the algebraic structure associated with them. The invariance of physical laws is a result of symmetry of the physical world that is often expressed by the symmetry of the state space for the system implying that this state space is a symmetric domain. For example, the ball of all possible velocities is a bounded symmetric domain. The symmetry on this ball follow from the symmetry of the space-time transformations between two inertial systems, which fixes the so-called symmetric velocity between them. The Lorenz transformations acts on the ball Sof symmetric velocities by conformal transformations. The ball Sis a spin ball (type IV in Cartan's classification). The Lie algebra of this ball is defined a triple product that is closely related to geometric product. The relativistic dynamic equations in mechanics and for the Lorenz force is described by this Lie algebra and the triple product.     

Why optical data communications and why now?

Our focus here is on data communications within IT equipment and in IT data centers. Optical communications is not new. Thus the obvious question is likely; why a paper entitled, “Why optical data communications and why now?”. The reasons are twofold. First, optical data communications is more necessary now than it has ever been in the past. It is not excessive to even consider that it will be required in the not too distant future. Second, the advances in the broad field of photonics and optics have brought optical communications nearly to the point that it can finally cross over the threshold to be less expensive than electronic signaling. In this paper we make the case why we must aggressively pursue optics for data communications at all length scales within the data center. The summarization of this paper is that optical communications is inevitable, and we offer reasons why we believe this is true.     

Why is polonium simple cubic and so highly anisotropic?

Using the state-of-the-art ab initio electronic structure calculations, we explain why alpha-Po prefers the simple cubic structure (it is due to the relativistic mass-velocity and Darwin terms), elucidate its extreme elastic anisotropy (this is an intrinsic property of the simple cubic crystal structure), and predict a transformation to a mixture of two trigonal structures at pressures of 1-3 GPa.     

Schwarzschild Radius Before General Relativity: Why Does Michell-Laplace Argument Provide the Correct Answer?

A famous Newtonian argument by Michell and Laplace, regarding the existence of “dark bodies” and dating back to the end of the 18th century, is able to provide an exact general-relativistic result, namely the exact formula for the Schwarzschild radius. Since general relativity was formulated more than a century after this argument had been issued, it looks quite surprising that such a correct prediction could have been possible. Far from being merely a fortuitous coincidence (as one might justifiably be induced to think), this fact can find a reasonable explanation once the question is approached the other way round, i.e. from the general-relativistic point of view. By reexamining Laplace’s proof from this point of view, we discuss here the reasons why Michell-Laplace argument can be so “unexpectedly" correct in its general-relativistic prediction.     

Why are large cities faster? Universal scaling and self-similarity in urban organization and dynamics

Cities have existed since the beginning of civilization and have always been intimately connected with humanity's cultural and technological development. Much about the human and social dynamics that takes place is cities is intuitively recognizable across time, space and culture; yet we still do not have a clear cut answer as to why cities exist or to what factors are critical to make them thrive or collapse. Here, we construct an extensive quantitative characterization of the variation of many urban indicators with city size, using large data sets for American, European and Chinese cities. We show that social and economic quantities, characterizing the creation of wealth and new ideas, show increasing returns to population scale, which appear quantitatively as a power law of city size with an exponent β≃ 1.15 > 1. Concurrently, quantities characterizing material infrastructure typically show economies of scale, namely β≃ 0.8 < 1. The existence of pervasive scaling relations across city size suggests a universal social dynamics common to all cities within an urban system. We sketch some of their general ingredients, which include the acceleration of social life and a restructuring of individual social networks as cities grow larger. We also build simple dynamical models to show that increasing returns in wealth and innovation can fuel faster than exponential growth, which inexorably lead to crises of urban organization. To avoid them we show that growth may proceed in cycles, separated by major urban adaptations, with the unintended consequence that the duration of such cycles decreases with larger urban population size and is now estimated to be shorter than a human lifetime.     

Does gravity cause load-bearing bridges in colloidal and granular systems?

We study structures which can bear loads, "bridges", in particulate packings. To investigate the relationship between bridges and gravity, we experimentally determine bridge statistics in colloidal packings. We vary the effective magnitude and direction of gravity, volume fraction, and interactions, and find that the bridge size distributions depend only on the mean number of neighbors. We identify a universal distribution, in agreement with simulation results for granulars, suggesting that applied loads merely exploit preexisting bridges, which are inherent in dense packings.     

Does bulk metallic glass of elemental Zr and Ti exist?

To verify the high-pressure formation of the bulk metallic glass in elemental Zr and Ti, which Zhang and Zhao [Nature (London) 430, 332 (2004)] and Y. Wang et al. [Phys. Rev. Lett. 95, 155501 (2005)] recently reported, the high-pressure states were investigated by our newly developed in situ angle-dispersive x-ray diffraction using a two-dimensional detector and x-ray transparent anvils. Despite the disappearance of all the Bragg peaks in the one-dimensional energy-dispersive data, two-dimensional angle-dispersive data showed several intense Bragg spots even at the conditions where the amorphization was reported. This finding suggests that Zr and Ti do not transform into an amorphous state, but that their grain size becomes large, which causes the missing Bragg peaks in energy-dispersive data.     

Why do active and stabilized dunes coexist under the same climatic conditions?

Sand dunes can be active (mobile) or stable, mainly as a function of vegetation cover and wind power. However, there exists as yet unexplained evidence for the coexistence of bare mobile dunes and vegetated stabilized dunes under the same climatic conditions. We propose a model for dune vegetation cover driven by wind power that exhibits bistabilty and hysteresis with respect to the wind power. For intermediate wind power, mobile and stabilized dunes can coexist, whereas for low (or high) wind power they can be fixed (or mobile). Climatic change or human intervention can turn active dunes into stable ones and vice versa; our model predicts that prolonged droughts with stronger winds can result in dune reactivation.     

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.     

Does Bd and Bs mixing require the existence of a fourth generation?

Recent data from ARGUS gives an unexpectedly large B_d mixing. Combined with a previous limit on B_d, B_s mixing from MARK II, it appears that while B_d mixing is sizeable, B_s mixing may be non-maximal. This may contradict the three-generation standard model. We discuss the implications of this possibility on the existence of a fourth generation. More reliable information can come from a study of B mixing at the

(5S) and is therefore strongly urged.     

Does membrane lipid profile explain chilling sensitivity and membrane lipid phase transition of spermatozoa and oocytes?

Ram, fowl and bee spermatozoa, and oocytes of cows and zebrafish were used to study lipid membrane profiles, chilling sensitivity and lipid-phase transitions. The integrity of the membranes was determined by carboxyfluorescein diacetate (CFDA) staining following exposure for 15 minutes to low temperatures. Ram and fowl spermatozoa showed different degrees of loss of membrane integrity. Surprisingly, bee spermatozoa did not show any sensitivity to chilling, and their membranes remained intact down to 0 degree C. In bovine oocytes (at the GV stage) chilling injury was very severe at 16 degree C (membrane integrity decreased by 50%). Lipid phase transition (LPT) and membrane fluidity, which were evaluated by Fourier transform infrared (FTIR) microscopy, and fluorescence polarisation, showed phase transitions at the same temperatures as caused damage (between 30 and 12 degree C). The membrane lipid profiles showed high concentrations of polyunsaturated fatty acids (PUFA) in cold-sensitive ram spermatozoa and zebrafish oocytes, but the ratio between PUFA and saturated fatty acids was highest in cold-resistant bee spermatozoa and lowest in cold-sensitive bovine oocytes. These results suggest a close relationship among cold susceptibility, lipid phase transition and lipids profile in animal gametes.     

Why can soil maintain its endless eco-cycle? The relationship between the mechanical properties and ecological attributes of soil

正Dear Editors,Why can soil maintain its endless eco-cycle to sustain the growth of plants?This mystery of nature can be resolved by revealing the relationship between the mechanical properties and ecological attributes of soil.Traditionally,soil science[1-3]is divided into two main categories:pedology and edaphology.In addition,soil mechanics addresses soil from the perspective of mechanics and engineering[4,5].However,no inherent relationship