A Data-Driven Approach for Discovering the Most Probable Transition Pathway for a Stochastic Carbon Cycle System

Many natural systems exhibit tipping points where changing environmental conditions spark a sudden shift to a new and sometimes quite different state. Global climate change is often associated with the stability of marine carbon stocks. We consider a stochastic carbonate system of the upper ocean to capture such transition phenomena. Based on the Onsager-Machlup action functional theory, we calculate the most probable transition pathway between the metastable and oscillatory states via a neural shooting method, and further explore the effects of external random carbon input rates on the most probable transition pathway, which provides a basis to recognize naturally occurring tipping points. Particularly, we investigate the effect of the transition time on the transition pathway and further compute the optimal transition time using physics informed neural network, towards the maximum carbonate concentration state in the oscillatory regimes. This work offers some insights on the effects of random carbon input on climate transition in a simple model. Key words: Onsager-Machlup action functional, the most probable transition pathway, neural shooting method, stochastic carbon cycle system

Most Probable Transitions from Metastable to Oscillatory Regimes in a Carbon Cycle System

Global climate changes are related to the ocean's store of carbon. We study a carbonate system of the upper ocean, which has metastable and oscillatory regimes, under small random fluctuations. We calculate the most probable transition path via a geometric minimum action method in the context of the large deviations theory. By examining the most probable transition paths from metastable to oscillatory regimes for various external carbon input rates, we find two different transition patterns, which gives us an early warning sign for the dramatic change in the carbonate state of the ocean

Leaderless Byzantine Fault Tolerant Consensus

Byzantine fault tolerant (BFT) consensus has recently gained much attention because of its intriguing connection with blockchains. Several state-of-the-art BFT consensus protocols have been proposed in the age of blockchains such as Tendermint [5], Pala [9], Streamlet [8], HotStuff [23], and Fast-HotStuff [17]. These protocols are all leader-based (i.e., protocols run in a series of views, and each view has a delegated node called the leader to coordinate all consensus decisions). To make progress, leader-based BFT protocols usually rely on view synchronization, which is an ad-hoc way of rotating the leader and synchronizing nodes to the same view with the leader for enough overlap time. However, many studies and system implementations show that existing methods of view synchronization are complicated and bug-prone [2], [15], [16], [19]. In this paper, we aim to design a leaderless Byzantine fault tolerant (LBFT) protocol, in which nodes simply compete to propose blocks (containing a batch of clients' requests) without the need of explicit coordination through view synchronization. LBFT also enjoys several other desirable features emphasized recently by the research community, such as the chain structure, pipelining techniques, and advanced cryptography [5], [6], [9], [17], [23]. With these efforts, LBFT can achieve both good performance (e.g., O(n)or O(nlog(n)) message complexity) and prominent simplicity.Comment: 13 page, 4 figure

Stylized Table Tennis Robots Skill Learning with Incomplete Human Demonstrations

In recent years, Reinforcement Learning (RL) is becoming a popular technique for training controllers for robots. However, for complex dynamic robot control tasks, RL-based method often produces controllers with unrealistic styles. In contrast, humans can learn well-stylized skills under supervisions. For example, people learn table tennis skills by imitating the motions of coaches. Such reference motions are often incomplete, e.g. without the presence of an actual ball. Inspired by this, we propose an RL-based algorithm to train a robot that can learn the playing style from such incomplete human demonstrations. We collect data through the teaching-and-dragging method. We also propose data augmentation techniques to enable our robot to adapt to balls of different velocities. We finally evaluate our policy in different simulators with varying dynamics.Comment: Submitted to ICRA 202