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Register a new userIn what sense space dimensionality can be used to cast light into cultural anthropology?
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Humans have always constructed spaces, through Mythos and Logos, as part of an aspiration to capture the essence of the changing world. This has been a permanent endeavour since the invention of language. By doing this, in fact, Humankind started constructing itself: we are beings in constant evolutionary process in real and imaginary spaces. Our concepts of Space and our anthropological ideas, specially the fundamental concepts of subject and subjectivity, are intertwined and intimately connected. We believe that the great narratives about Humanity, which ultimately define our view of ourselves, are entangled with those concepts that Cassirer identified as the cornerstones of culture: space, time, and number. To explore these ideas, the authors wrote an essay, in 2017, in a book format, in which the fundamental role of real and imaginary spaces (and especially of their dimensionalities) in the History of Culture was discussed. This book, titled O Livro, o Espac{c}o e a Natureza: Ensaio Sobre a Leitura do Mundo, as Mutac{c}~oes da Cultura e do Sujeito, has a preface written by Francisco Antonio Doria. As many of the issues treated there are among his multiple interests, it was decided to revisit here the problems of subjectivity and subjects relationship with the dimensionality of space including the question of the architecture of books and other writing supports.
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(Abridged) This paper studies chaotic orbit ensembles evolved in triaxial generalisations of the Dehnen potential which have been proposed to model ellipticals with a strong density cusp that manifest significant deviations from axisymmetry. Allowance is made for a possible supermassive black hole, as well as low amplitude friction, noise, and periodic driving which can mimic irregularities associated with discreteness effects and/or an external environment. The degree of chaos is quantified by determining how (1) the relative number of chaotic orbits and (2) the size of the largest Lyapunov exponent depend on the steepness of the cusp and the black hole mass, and (3) the extent to which Arnold webs significantly impede phase space transport, both with and without perturbations. In the absence of irregularities, chaotic orbits tend to be extremely `sticky, so that different pieces of the same chaotic orbit can behave very differently for 10000 dynamical times or longer, but even very low amplitude perturbations can prove efficient in erasing many -- albeit not all -- these differences. The implications thereof are discussed both for the structure and evolution of real galaxies and for the possibility of constructing approximate near-equilibrium models using Schwarzschilds method. Much of the observed qualitative behaviour can be reproduced with a toy potential given as the sum of an anisotropic harmonic oscillator and a spherical Plummer potential, which suggests that the results may be generic.
Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.
Predicting the execution time of queries is an important problem with applications in scheduling, service level agreements and error detection. During query planning, a cost is associated with the chosen execution plan and used to rank competing plans. It would be convenient to use that cost to predict execution time, but it has been claimed in the literature that this is not possible. In this paper, we thoroughly investigate this claim considering both linear and non-linear models. We find that the accuracy using more complex models with only the optimizer cost is comparable to the reported accuracy in the literature. The most accurate method in the literature is nearest-neighbour regression which does not produce a model. The published results used a large feature set to identify nearest neighbours. We show that it is possible to achieve the same level of accuracy using only the cost to identify nearest neighbours. Using a smaller feature set brings the advantages of reduced overhead in terms of both storage space for the training data and the time to produce a prediction.
Forking structure is widespread in the open-source repositories and that causes a significant number of merge conflicts. In this paper, we study the problem of textual merge conflicts from the perspective of Microsoft Edge, a large, highly collaborative fork off the main Chromium branch with significant merge conflicts. Broadly, this study is divided into two sections. First, we empirically evaluate textual merge conflicts in Microsoft Edge and classify them based on the type of files, location of conflicts in a file, and the size of conflicts. We found that ~28% of the merge conflicts are 1-2 line changes, and many resolutions have frequent patterns. Second, driven by these findings, we explore Program Synthesis (for the first time) to learn patterns and resolve structural merge conflicts. We propose a novel domain-specific language (DSL) that captures many of the repetitive merge conflict resolution patterns and learn resolution strategies as programs in this DSL from example resolutions. We found that the learned strategies can resolve 11.4% of the conflicts (~41% of 1-2 line changes) that arise in the C++ files with 93.2% accuracy.
Hydrogen atom is supposed to be described by a generalization of Schrodinger equation, in which the Hamiltonian depends on an iterated Laplacian and a Coulomb-like potential $r^{-beta}$. Starting from previously obtained solutions for this equation using the $1/N$ expansion method, it is shown that new light can be shed on the problem of understanding the dimensionality of the world as proposed by Paul Ehrenfest. A surprisingly new result is obtained. Indeed, for the first time, we can understand that not only the sign of energy but also the value of the ground state energy of hydrogen atom is related to the threefold nature of space.
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