Inverse Reinforcement Learning for Discrete-Time Systems With Data Dropouts.

This article proposes inverse reinforcement learning (IRL) algorithms for tracking control of linear networked control systems under random state dropouts during wireless transmission. The controlled system aims to track the optimal trajectory of a target system, despite the cost function governing the target's behaviors being unknown. The problem is complicated by random state dropouts occurring in two crucial scenarios: 1) the reception of the target's state and 2) feedback of the controlled system's states.

The role of the phonological loop and the visual sketchpad in speechreading of students with hearing loss in China.

The study examined the role of two slave systems of working memory (WM), the phonological loop and the visuospatial sketchpad, in the speechreading performance of Chinese students with hearing loss (HL). It was motivated by the question whether the visual speech information is processed in the phonological loop as linguistic information or as visuospatial information in visuospatial sketchpad. Seventy-three young adults with HL completed Chinese speech-reading tests (targeting monosyllabic words, disyllabic words, and sentences), the WM test batteries, and a cognitive processing speed test.

Genomes of diverse Actinidia species provide insights into cis-regulatory motifs and genes associated with critical traits.

Background: Kiwifruit, belonging to the genus Actinidia, represents a unique fruit crop characterized by its modern cultivars being genetically diverse and exhibiting remarkable variations in morphological traits and adaptability to harsh environments. However, the genetic mechanisms underlying such morphological diversity remain largely elusive.

Results: We report the high-quality genomes of five Actinidia species, including Actinidia longicarpa, A.

Histone acetyltransferase Sas3 contributes to fungal development, cell wall integrity, and virulence in .

Unlabelled: Histone acetyltransferase (HAT)-mediated epigenetic modification is essential for diverse cellular processes in eukaryotes. However, the functions of HATs in the human pathogen remain poorly understood. In this study, we characterized the functions of OZ, bf2/as3, Sas2, and ip60 (MYST)-family histone acetyltransferase something about silencing (Sas3) in .

Inverse Reinforcement Learning for Trajectory Imitation Using Static Output Feedback Control.

This article studies the trajectory imitation control problem of linear systems suffering external disturbances and develops a data-driven static output feedback (OPFB) control-based inverse reinforcement learning (RL) approach. An Expert-Learner structure is considered where the learner aims to imitate expert's trajectory. Using only measured expert's and learner's own input and output data, the learner computes the policy of the expert by reconstructing its unknown value function weights and thus, imitates its optimally operating trajectory.

Aspergillus fumigatus Elongator complex subunit 3 affects hyphal growth, adhesion and virulence through wobble uridine tRNA modification.

The eukaryotic multisubunit Elongator complex has been shown to perform multiple functions in transcriptional elongation, histone acetylation and tRNA modification. However, the Elongator complex plays different roles in different organisms, and the underlying mechanisms remain unexplored. Moreover, the biological functions of the Elongator complex in human fungal pathogens remain unknown.

Data-Driven H Optimal Output Feedback Control for Linear Discrete-Time Systems Based on Off-Policy Q-Learning.

This article develops two novel output feedback (OPFB) Q -learning algorithms, on-policy Q -learning and off-policy Q -learning, to solve H static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed algorithms lies in a newly developed OPFB control algorithm form for completely unknown systems. Under the premise of satisfying disturbance attenuation conditions, the conditions for the existence of the optimal OPFB solution are given.

Inverse Reinforcement Q-Learning Through Expert Imitation for Discrete-Time Systems.

In inverse reinforcement learning (RL), there are two agents. An expert target agent has a performance cost function and exhibits control and state behaviors to a learner. The learner agent does not know the expert's performance cost function but seeks to reconstruct it by observing the expert's behaviors and tries to imitate these behaviors optimally by its own response.

Two Lysine Sites That Can Be Malonylated Are Important for LuxS Regulatory Roles in .

S-ribosylhomocysteine lyase (LuxS) has been shown to regulate bacterial multicellular behaviors, typically biofilm formation. However, the mechanisms for the regulation are still mysterious. We previously identified a malonylation modification on K124 and K130 of the LuxS in the plant growth-promoting rhizobacterium   (FZB42).

Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control.

This article provides a novel inverse reinforcement learning (RL) algorithm that learns an unknown performance objective function for tracking control. The algorithm combines three steps: 1) an optimal control update; 2) a gradient descent correction step; and 3) an inverse optimal control (IOC) update. The new algorithm clarifies the relation between inverse RL and IOC.

The fungal-specific histone acetyltransferase Rtt109 regulates development, DNA damage response, and virulence in Aspergillus fumigatus.

In eukaryotes, histone acetylation catalyzed by histone acetyltransferase (HAT) has been demonstrated to be critical for various physiological processes. However, the biological functions of HAT and the underlying mechanism by which HAT-regulated processes are involved in fungal development and virulence in the human opportunistic pathogen Aspergillus fumigatus remain largely unexplored. Here, we functionally characterized the roles of Rtt109 in A.

Off-Policy Reinforcement Learning for Tracking in Continuous-Time Systems on Two Time Scales.

This article applies a singular perturbation theory to solve an optimal linear quadratic tracker problem for a continuous-time two-time-scale process. Previously, singular perturbation was applied for system regulation. It is shown that the two-time-scale tracking problem can be separated into a linear-quadratic tracker (LQT) problem for the slow system and a linear-quadratic regulator (LQR) problem for the fast system.

Aspergillus fumigatus Mitochondrial Acetyl Coenzyme A Acetyltransferase as an Antifungal Target.

Ergosterol plays an important role in maintaining cell membrane sterol homeostasis in fungi, and as such, it is considered an effective target in antifungal chemotherapy. In yeast, the enzyme acetyl-coenzyme A (CoA) acetyltransferase (ERG10) catalyzes the Claisen condensation of two acetyl-CoA molecules to acetoacetyl-CoA in the ergosterol biosynthesis pathway and is reported as being critical for cell viability. Using yeast ERG10 for alignment, two orthologues, ERG10A (AFUB_000550) and ERG10B (AFUB_083570), were discovered in the opportunistic fungal pathogen Despite the essentiality of ERG10B having been previously validated, the biological function of ERG10A remains unclear.

Optimal Output Regulation of Linear Discrete-Time Systems With Unknown Dynamics Using Reinforcement Learning.

This paper presents a model-free optimal approach based on reinforcement learning for solving the output regulation problem for discrete-time systems under disturbances. This problem is first broken down into two optimization problems: 1) a constrained static optimization problem is established to find the solution to the output regulator equations (i.e.

Tracking Control for Linear Discrete-Time Networked Control Systems With Unknown Dynamics and Dropout.

This paper develops a new method for solving the optimal control tracking problem for networked control systems (NCSs), where network-induced dropout can occur and the system dynamics are unknown. First, a novel dropout Smith predictor is designed to predict the current state based on historical data measurements over the communication network. Then, it is shown that the quadratic form of the performance index is preserved even with dropout, and the optimal tracker solution with dropout is given based on a novel dropout generalized algebraic Riccati equation.

Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled.

Performance-Based Adaptive Fuzzy Tracking Control for Networked Industrial Processes.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system.