On the impact of covariance functions in multi-objective Bayesian optimization for engineering design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMulti-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.Natural Environment Research Council (NERC

Sequence tutor: Conservative fine-tuning of sequence generation models with KL-control

This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data

Stellar equilibrium configurations of white dwarfs in the f(R,T)f(R,T) gravity

In this work we investigate the equilibrium configurations of white dwarfs in a modified gravity theory, na\-mely, $f(R,T)$ gravity, for which $R$ and $T$ stand for the Ricci scalar and trace of the energy-momentum tensor, respectively. Considering the functional form $f(R,T)=R+2\lambda T$, with $\lambda$ being a constant, we obtain the hydrostatic equilibrium equation for the theory. Some physical properties of white dwarfs, such as: mass, radius, pressure and energy density, as well as their dependence on the parameter $\lambda$ are derived. More massive and larger white dwarfs are found for negative values of $\lambda$ when it decreases. The equilibrium configurations predict a maximum mass limit for white dwarfs slightly above the Chandrasekhar limit, with larger radii and lower central densities when compared to standard gravity outcomes. The most important effect of $f(R,T)$ theory for massive white dwarfs is the increase of the radius in comparison with GR and also $f(R)$ results. By comparing our results with some observational data of massive white dwarfs we also find a lower limit for $\lambda$, namely, $\lambda >- 3\times 10^{-4}$.Comment: To be published in EPJ

Mitochondria mediate septin cage assembly to promote autophagy of Shigella

Septins, cytoskeletal proteins with well-characterised roles in cytokinesis, form cage-like structures around cytosolic Shigella flexneri and promote their targeting to autophagosomes. However, the processes underlying septin cage assembly, and whether they influence S. flexneri proliferation, remain to be established. Using single-cell analysis, we show that the septin cages inhibit S. flexneri proliferation. To study mechanisms of septin cage assembly, we used proteomics and found mitochondrial proteins associate with septins in S. flexneri-infected cells. Strikingly, mitochondria associated with S. flexneri promote septin assembly into cages that entrap bacteria for autophagy. We demonstrate that the cytosolic GTPase dynamin-related protein 1 (Drp1) interacts with septins to enhance mitochondrial fission. To avoid autophagy, actin-polymerising Shigella fragment mitochondria to escape from septin caging. Our results demonstrate a role for mitochondria in anti-Shigella autophagy and uncover a fundamental link between septin assembly and mitochondria

A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in L2, and the Fisher-Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the alpha-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Frechet derivative operators motivated by the geometry of the Hilbert sphere, and examine its properties. Through simulations and real-data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models