continuous space
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Abstract We investigate the problem of minimizing the entropy production for a physical process that can be described in terms of a Markov jump dynamics. We show that, without any further constraints, a given time-evolution may be realized at arbitrarily small entropy production, yet at the expense of diverging activity. For a fixed activity, we find that the dynamics that minimizes the entropy production is given in terms of conservative forces. The value of the minimum entropy production is expressed in terms of the graph-distance based Wasserstein distance between the initial and final configuration. This yields a new kind of speed limit relating dissipation, the average number of transitions and the Wasserstein distance. It also allows us to formulate the optimal transport problem on a graph in term of a continuous-time interpolating dynamics, in complete analogy to the continuous space setting. We demonstrate our findings for simple state networks, a time-dependent pump and for spin flips in the Ising model.

Based on the discovery by astrophysicists of dark matter halos around galaxies, stars and planets, it became possible to abandon the speculative concept of the spatial curvature of Einstein's space-time fabric and geometric gravity. Torsional gravity and spinors in fundamental theoretical physics should be based on a new cosmology, including a dark matter halo rotating with planets, stars and galaxies and forming funnels in the continuous space environment of a quantum vacuum (dark matter). The article discusses the nature of tornado and tropical hurricanes.

Abstract Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a numerically costly black-box simulation. General mixed and costly optimization problems are therefore of a great practical interest, yet their resolution remains in a large part an open scientific question. In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables. The continuous space is more easily harvested by classical Bayesian optimization techniques than a mixed space would. Discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented Lagrangians. Several possible implementations of such Bayesian mixed optimizers are compared. In particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. Among the algorithms involving latent variables and an augmented Lagrangian, a particular attention is devoted to the Lagrange multipliers for which a local and a global estimation techniques are studied. The comparisons are based on the repeated optimization of three analytical functions and a beam design problem.

Abstract The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for the analysis of continuous space. First, small wave basis ANN algorithm is proposed to solve discretization strategy in continuous space: The hidden layer node transfer function in BP neural network is substituted with wavelet basis function, while the replaced BP neural network is composed of wavelet neural network. Secondly, improved wolf algorithm is set up. The core wolf system ensures the precision of whole exploration. Finally, the main and auxiliary double cores and five-class decision factor is used to establish a population classification model to solve the convergence of the algorithm.

Gene drives have shown great promise for suppression of pest populations. These engineered alleles can function by a variety of mechanisms, but the most common is the CRISPR homing drive, which converts wild-type alleles to drive alleles in the germline of heterozygotes. Some potential target species are haplodiploid, in which males develop from unfertilized eggs and thus have only one copy of each chromosome. This prevents drive conversion, a substantial disadvantage compared to diploids where drive conversion can take place in both sexes. Here, we study the characteristics of homing suppression gene drives in haplodiploids and find that a drive targeting a female fertility gene could still be successful. However, such drives are less powerful than in diploids. They are substantially more vulnerable to high resistance allele formation in the embryo due to maternally deposited Cas9 and gRNA and also to somatic cleavage activity. Examining models of continuous space where organisms move over a landscape, we find that haplodiploid suppression drives surprisingly perform nearly as well as in diploids, possibly due to their ability to spread further before inducing strong suppression. Together, these results indicate that gene drive can potentially be used to effectively suppress haplodiploid populations.

Based on the results from black hole thermodynamics at all energy scales, this work demonstrates that, both for the discrete QFT previously introduced by the author and for QFT in continuous space-time, there is a natural ultraviolet applicable boundary (cut-off) distant from the Planck scales. It is important that this boundary exists irrespective of the fact in which pattern, perturbative or non-perturbative mode, QFT is studied. Different inferences from the obtained results are discussed, some statements are revised.

Abstract Data-driven based deep learing has become a key research direction in the field of artificial intelligence. Abundant training data is a guarantee for building efficient and accurate models. However, due to the privacy protection policy, research institutions are often limited to obtain a large number of training data, which would lead to a lack of training sets circumstance. In this paper, a mixed data generation model (mixGAN) based on generative adversarial networks (GANs) is proposed to synthesize fake data that have the same distribution with the real data, so as to supplement the real data and increase the number of available samples. The model first pre-trains the autoencoder which maps given dataset into a low-dimensional continuous space. Then, the Generator constructed in the low-dimension space is obtained by training it adversarially with Discriminator constructed in the original space. Since the constructed Discriminator not only consider the loss of the continuous attributes but also the labeled attributes, the generator nets formed by the Generator and the decoder can effectively learn the intrinsic distribution of the mixed data. We evaluate the proposed method both in the independent distribution of the attribute and in the relationship of the attributes, and the experiment results show that the proposed generate method has a better performance in preserve the intrinsic distribution compared with other generation algorithms based on deep learning.