Pontryagin's Minimum Principle-Guided RL for Minimum-Time Exploration of Spatiotemporal Fields.

This article studies the trajectory planning problem of an autonomous vehicle for exploring a spatiotemporal field subject to a constraint on cumulative information. Since the resulting problem depends on the signal strength distribution of the field, which is unknown in practice, we advocate the use of a model-free reinforcement learning (RL) method to find the solution. Given the vehicle's dynamical model, a critical (and open) question is how to judiciously merge the model-based optimality conditions into the model-free RL framework for improved efficiency and generalization, for which this work provides some positive results. Specifically, we discretize the continuous action space by leveraging analytic optimality conditions for the minimum-time optimization problem via Pontryagin's minimum principle (PMP). This allows us to develop a novel discrete PMP-based RL trajectory planning algorithm, which learns a planning policy faster than those based on a continuous action space. Simulation results: 1) validate the effectiveness of the PMP-based RL algorithm and 2) demonstrate its advantages, in terms of both learning efficiency and the vehicle's exploration time, over two baseline methods for continuous control inputs.

Informative Trajectory Planning Using Reinforcement Learning for Minimum-Time Exploration of Spatiotemporal Fields.

This article studies the informative trajectory planning problem of an autonomous vehicle for field exploration. In contrast to existing works concerned with maximizing the amount of information about spatial fields, this work considers efficient exploration of spatiotemporal fields with unknown distributions and seeks minimum-time trajectories of the vehicle while respecting a cumulative information constraint. In this work, upon adopting the observability constant as an information measure for expressing the cumulative information constraint, the existence of a minimum-time trajectory is proven under mild conditions. Given the spatiotemporal nature, the problem is modeled as a Markov decision process (MDP), for which a reinforcement learning (RL) algorithm is proposed to learn a continuous planning policy. To accelerate the policy learning, we design a new reward function by leveraging field approximations, which is demonstrated to yield dense rewards. Simulations show that the learned policy can steer the vehicle to achieve an efficient exploration, and it outperforms the commonly-used coverage planning method in terms of exploration time for sufficient cumulative information.

Association between indel polymorphism (rs145204276) in the promoter region of lncRNA GAS5 and the risk of febrile convulsion.

BACKGROUND: This study aimed to explore the regulatory relationship between growth arrest special 5 (GAS5) and interleukin-1ß (IL-1ß) implicated in the development of febrile seizure (FS). METHOD: The presence of FS and the genotype of GAS5 were used as two different indicators to divide the 50 newborn babies, recruited in this study, into different groups. The potential regulatory relationship among GAS5, miR-21, and IL-1ß was identified by measuring their expression using quantitative reverse-transcription polymerase chain reaction and immunohistochemistry assays among different sample groups. Computational analyses and luciferase assays were also conducted to verify the interaction between GAS5, miR-21, and IL-1ß. RESULT: GAS5 and IL-1ß expression was upregulated in cells collected from FS patients or genotyped as INS/DEL and DEL/DEL, whereas the expression of miR-21 was decreased in above samples, indicating a negative relationship between miR-21 and GAS5/IL-1ß. Results of the computational analysis showed that miR-21 directly bound to and increased the expression of GAS5, whereas the expression of IL-1ß was suppressed by miR-21. In the presence of GAS5, the expression of miR-21 was lowered, whereas the expression of IL-1ß was increased. CONCLUSION: The results obtained in this study supported the conclusion that GAS5 negatively regulated the expression of miR-21, which in turn negatively regulated the expression IL-1ß. Therefore, the overexpression of GAS5 could decrease the magnitude of FS.

Trends in extreme learning machines: a review.

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

Robust support vector regression for uncertain input and output data.

In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two linear robust formulations are derived. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective. Second, kernelized RSVR formulations are established for nonlinear regression problems. Both linear and nonlinear formulations are converted to second-order cone programming problems, which can be solved efficiently by the interior point method. Simulation demonstrates that the proposed method outperforms existing RSVRs in the presence of both input and output data uncertainties.