Two-Layer Predictive Control of a Continuous Biodiesel Transesterification Reactor

A novel two-layer predictive control scheme for a continuous biodiesel transesterification reactor is presented. Based on a validated mechanistic model, the least squares (LS) algorithm is used to identify the finite step response (FSR) process model adapted in the controller. The two-layer predictive control method achieves the steady-state optimal setpoints and resolves the multivariable dynamic control problems synchronously. Simulation results show that the two-layer predictive control strategy leads to a significant improvement of control performance in terms of the optimal set-points tracking and disturbances rejection, as compared to conventional PID controller within a multiloop framework

Quantum reinforcement learning in continuous action space

Quantum mechanics has the potential to speedup machine learning algorithms, including reinforcement learning(RL). Previous works have shown that quantum algorithms can efficiently solve RL problems in discrete action space, but could become intractable in continuous domain, suffering notably from the curse of dimensionality due to discretization. In this work, we propose an alternative quantum circuit design that can solve RL problems in continuous action space without the dimensionality problem. Specifically, we propose a quantum version of the Deep Deterministic Policy Gradient method constructed from quantum neural networks, with the potential advantage of obtaining an exponential speedup in gate complexity for each iteration. As applications, we demonstrate that quantum control tasks, including the eigenvalue problem and quantum state generation, can be formulated as sequential decision problems and solved by our method.Comment: 9 pages, 8 figure

Frosting Weights for Better Continual Training

Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one appealing property is the additive nature of ensemble models. In this paper, we propose two generic ensemble approaches, gradient boosting and meta-learning, to solve the catastrophic forgetting problem in tuning pre-trained neural network models

Biosynthesis of thiocarboxylic acid-containing natural products.

Thiocarboxylic acid-containing natural products are rare and their biosynthesis and biological significance remain unknown. Thioplatensimycin (thioPTM) and thioplatencin (thioPTN), thiocarboxylic acid congeners of the antibacterial natural products platensimycin (PTM) and platencin (PTN), were recently discovered. Here we report the biosynthetic origin of the thiocarboxylic acid moiety in thioPTM and thioPTN. We identify a thioacid cassette encoding two proteins, PtmA3 and PtmU4, responsible for carboxylate activation by coenzyme A and sulfur transfer, respectively. ThioPTM and thioPTN bind tightly to β-ketoacyl-ACP synthase II (FabF) and retain strong antibacterial activities. Density functional theory calculations of binding and solvation free energies suggest thioPTM and thioPTN bind to FabF more favorably than PTM and PTN. Additionally, thioacid cassettes are prevalent in the genomes of bacteria, implicating that thiocarboxylic acid-containing natural products are underappreciated. These results suggest that thiocarboxylic acid, as an alternative pharmacophore, and thiocarboxylic acid-containing natural products may be considered for future drug discovery

A Target Sequential Effect on the Forced-Choice Prime Visibility Test in Unconscious Priming Studies: A Caveat for Researchers

In unconscious priming studies, most researchers adopt a combination of subjective and objective measures to assess the visibility of the prime. Although some carry out the visibility test at the end of the experiment separately from the unconscious priming task, others suggest that the forced-choice visibility test should be conducted immediately after the response to the target within each trial. In the present study, the influence of prime and target on the forced-choice prime discrimination was assessed within each trial. The results showed that the target affected the response in the forced-choice prime visibility test. Participants tended to make the same response or avoid repeating the same response they made to the target as in Experiments 1 and 3 rather than randomly guessing. However, even when the forcedchoice visibility test was conducted separately from the priming experiment, the problem was not completely solved, because some participants tended to make one same response in the forced-choice visibility test as in Experiments 2. From another point of view, using these strategies in the forced-choice task can be seen as a helpless move by the participants when they are unaware of the stimuli. Furthermore, the results revealed that the forced-choice test performed immediately after the response to the target within each trial could possibly impair the unconscious priming as well as produce misleading visibility test results. Therefore, it is suggested that the forced-choice prime visibility test and the unconscious priming task may better be conducted separately

Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection

LAC: Practical Ring-LWE Based Public-Key Encryption with Byte-Level Modulus

We propose an instantiation of public key encryption scheme based on the ring learning with error problem, where the modulus is at a byte level and the noise is at a bit level, achieving one of the most compact lattice based schemes in the literature. The main technical challenges are a) the decryption error rates increases and needs to be handled elegantly, and b) we cannot use the Number Theoretic Transform (NTT) technique to speed up the implementation. We overcome those limitations with some customized parameter sets and heavy error correction codes. We give a treatment of the concrete security of the proposed parameter set, with regards to the recent advance in lattice based cryptanalysis. We present an optimized implementation taking advantage of our byte level modulus and bit level noise. In addition, a byte level modulus allows for high parallelization and the bit level noise avoids the modulus reduction during multiplication. Our result shows that \LAC~is more compact than most of the existing (Ring-)LWE based solutions, while achieving a similar level of efficiency, compared with popular solutions in this domain, such as Kyber

Proteomic and metabolomic analyses uncover integrative mechanisms in Sesuvium portulacastrum tolerance to salt stress

IntroductionSalt stress is a major constraint affecting crop productivity worldwide. Investigation of halophytes could provide valuable information for improving economically important crops to tolerate salt stress and for more effectively using halophytes to remediate saline environments. Sesuvium portulacastrum L. is a halophyte species widely distributed in tropical and subtropical coastal regions and can absorb a large amount of sodium (Na). This study was to analyze S. portulacastrum responses to salt stress at morphological, physiological, proteomic, and metabolomic levels and pursue a better understanding of mechanisms behind its salt tolerance. MethodsThe initial experiment evaluated morphological responses of S. portulacastrum to different concentrations of NaCl in a hydroponic system, and subsequent experiments compared physiological, proteomic, and metabolomic changes in S. portulacastrum after being exposed to 0.4 M NaCl for 24 h as immediate salt stress (IS) to 14 days as adaptive salt stress (AS). Through these analyses, a working model to illustrate the integrative responses of S. portulacastrum to salt stress was proposed.ResultsPlants grown in 0.4 M NaCl were morphologically comparable to those grown in the control treatment. Physiological changes varied in control, IS, and AS plants based on the measured parameters. Proteomic analysis identified a total of 47 and 248 differentially expressed proteins (DEPs) in leaves and roots, respectively. KEGG analysis showed that DEPs, especially those occurring in roots, were largely related to metabolic pathways. Root metabolomic analysis showed that 292 differentially expressed metabolites (DEMs) occurred in IS plants and 371 in AS plants. Among them, 20.63% of upregulated DEMs were related to phenolic acid metabolism. DiscussionBased on the integrative analysis of proteomics and metabolomics, signal transduction and phenolic acid metabolism appeared to be crucial for S. portulacastrum to tolerate salt stress. Specifically, Ca2+, ABA, and JA signalings coordinately regulated salt tolerance in S. portulacastrum. The stress initially activated phenylpropanoid biosynthesis pathway through Ca2+ signal transduction and increased the content of metabolites, such as coniferin. Meanwhile, the stress inhibited MAPK signaling pathway through ABA and JA signal transduction, which promoted Na sequestration into the vacuole to maintain ROS homeostasis and enhanced S. portulacastrum tolerance to salt stress