Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how the branch of tourism studies can better adapt to the development of the tourism industry. The research findings indicate that the BP model can be applied to the predictions of the scope of the tourism discipline and provide a quantitative basis for decision making with regard to the spatial layout and optimal allocation of the tourism discipline

Sequential Predictions based on Algorithmic Complexity

This paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff's universal prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is measured in terms of convergence of posteriors or losses. Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction. We show that for deterministic computable environments, the "posterior" and losses of m converge, but rapid convergence could only be shown on-sequence; the off-sequence convergence can be slow. In probabilistic environments, neither the posterior nor the losses converge, in general.Comment: 26 pages, LaTe

On Observational Predictions from Multidimensional Gravity

We discuss possible observational manifestations of static, spherically symmetric solutions of a class of multidimensional theories of gravity, which includes the low energy limits of supergravities and superstring theories as special cases. We discuss the choice of a physical conformal frame to be used for the description of observations. General expressions are given for (i) the Eddington parameters $\beta$ and $\gamma$, characterizing the post-Newtonian gravitational field of a central body, (ii) p-brane black hole temperatures in different conformal frames and (iii) the Coulomb law modified by extra dimensions. It is concluded, in particular, that $\beta$ and $\gamma$ depend on the integration constants and can be therefore different for different central bodies. If, however, the Einstein frame is adopted for describing observations, we always obtain $\gamma=1$. The modified Coulomb law is shown to be independent of the choice of a 4-dimensional conformal frame. We also argue the possible existence of specific multidimensional objects, T-holes, potentially observable as bodies with mirror surfaces.Comment: 16 pages, Latex2

Supergravity Predictions on Conformal Field Theories

We give an update on recent results about the matching between CFT operators and KK states in the AdS/CFT correspondence, and add some new comments on the realization of the baryonic symmetries from the supergravity point of view.Comment: 8 pages, uses JHEP.cls, Contribution to the proceedings of the TMR Conference on Quantum Aspects of Gauge Theories, Supersymmetry and Unification, Paris, 1-7 September 199

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.Comment: 17 pages, 19 figures, in Proceedings of ACM FAT* 2019, dataset & demo available at https://deception.machineintheloop.co