Comparing NARS and Reinforcement Learning: An Analysis of ONA and QQ-Learning Algorithms

In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.Comment: Accepted in the 16th AGI Conference (AGI-23), Stockholm, Sweden, June 16 - June 19, 2023. arXiv admin note: text overlap with arXiv:2212.1251

Human-Inspired Framework to Accelerate Reinforcement Learning

While deep reinforcement learning (RL) is becoming an integral part of good decision-making in data science, it is still plagued with sample inefficiency. This can be challenging when applying deep-RL in real-world environments where physical interactions are expensive and can risk system safety. To improve the sample efficiency of RL algorithms, this paper proposes a novel human-inspired framework that facilitates fast exploration and learning for difficult RL tasks. The main idea is to first provide the learning agent with simpler but similar tasks that gradually grow in difficulty and progress toward the main task. The proposed method requires no pre-training phase. Specifically, the learning of simpler tasks is only done for one iteration. The generated knowledge could be used by any transfer learning, including value transfer and policy transfer, to reduce the sample complexity while not adding to the computational complexity. So, it can be applied to any goal, environment, and reinforcement learning algorithm - both value-based methods and policy-based methods and both tabular methods and deep-RL methods. We have evaluated our proposed framework on both a simple Random Walk for illustration purposes and on more challenging optimal control problems with constraint. The experiments show the good performance of our proposed framework in improving the sample efficiency of RL-learning algorithms, especially when the main task is difficult

Comparison of the cardiovascular presentations, complications and outcomes following different coronaviruses' infection: A systematic review

Manifestations caused by coronavirus family have presented it in many ways during the previous years. The aim of this systematic review was to gather all possible cardiovascular manifestations of the coronavirus family in the literature. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched PubMed, Scopus, Web of Science, Cochrane and ProQuest which were updated on May 1, 2020 for the last time. Regarding to the novelty and speed of publications on COVID-19, we searched Google Scholar and also references of included studies and review articles in the systematic search results were searched manually. The searched keywords were the combination of the following MeSH terms: "COVID-19", "SARS", "MERS"and "cardiovascular presentation". The systematic review was registered with ID CRD42020180736 in International Prospective Register of Systematic Reviews (PROSPERO). After screening, 28 original articles and ten case studies (five case reports and five case series) were included. Most of the studies were focused on COVID-19 (20 original articles and five case studies) while the only studies about Middle East Respiratory Syndrome (MERS) was a case report. Almost all the cardiovascular presentations and complications including acute cardiac injury, arrhythmias and the thrombotic complications were more prevalent in COVID-19 than severe acute respiratory syndrome (SARS) and MERS. The cardiac injury was the most common cardiovascular presentation and complication in COVID-19 whereas thrombotic complications were commonly reported in SARS. The cardiac injury was the predictor of disease severity and mortality in both COVID-19 and SARS. Coronavirus 2019 may present with cardiovascular manifestations and complications in signs and symptoms, laboratory data and other paraclinical findings. Also, cardiovascular complications in the course of COVID-19 may result in worse outcomes. © 2021 The Author(s)

Novel and emerging mutations of SARS-CoV-2: Biomedical implications

Coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 virus strains has geographical diversity associated with diverse severity, mortality rate, and response to treatment that were characterized using phylogenetic network analysis of SARS-CoV-2 genomes. Although, there is no explicit and integrative explanation for these variations, the genetic arrangement, and stability of SARS-CoV-2 are basic contributing factors to its virulence and pathogenesis. Hence, understanding these features can be used to predict the future transmission dynamics of SARS-CoV-2 infection, drug development, and vaccine. In this review, we discuss the most recent findings on the mutations in the SARS-CoV-2, which provide valuable information on the genetic diversity of SARS-CoV-2, especially for DNA-based diagnosis, antivirals, and vaccine development for COVID-19. © 202

Corrigendum to: �Novel and emerging mutations of SARS-CoV-2: Biomedical implications� Biomed. Pharmacother. 139 (2021) 111599 (Biomedicine & Pharmacotherapy (2021) 139, (S075333222100384X), (10.1016/j.biopha.2021.111599))

The authors regret the incorrect publication of affiliations of some of the authors in the original article. The correct affiliation of the authors are presented below: Elmira Mohammadia,b Fatemeh Shafieec Kiana Shahzamanid Mohammad Mehdi Ranjbare Abbas Alibakhshif Shahrzad Ahangarzadehg Leila Beikmohammadih,i Laleh Shariatij,k Soodeh Hooshmandil Behrooz Ataeim Shaghayegh HaghjooyJavanmarda a Applied Physiology Research Center, Cardiovascular Research Institute, Department of Physiology, Isfahan University of Medical Sciences, Isfahan, Iran b Core Research Facilities, Isfahan University of Medical Sciences, Isfahan, Iran c Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran d Isfahan Gastroenterology and Hepatology Research Center (lGHRC), Isfahan University of medical sciences, Isfahan, Iran e Razi Vaccine and Serum Research Institute, Agricultural Research, Education, and Extension Organization (AREEO), Karaj, Iran f Molecular Medicine Research Center, Hamadan University of Medical Sciences, Hamadan, Iran g Infectious Diseases and Tropical Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran h Department of Biochemistry, Erasmus University Medical Center, Rotterdam, The Netherlands i Stem Cell and Regenerative Medicine Center of Excellence, Tehran University of Medical Sciences, 14155-6559 Tehran, Iran j Biosensor Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran k Department of Biomaterials, Nanotechnology and Tissue Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran l Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran m Nosocomial Infection Research Center, Isfahan University of Medical Sciences, Isfahan, Iran The authors would like to apologise for any inconvenience caused. © 202

Argumentative writing behavior of graduate EFL learners

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This study analyzed the argumentative writing behavior of Iranian graduate learners of English as Foreign Language in their English essays. Further, the correlations between the use of argument elements and overall writing quality as well as soundness of produced arguments were investigated. To this end, 150 essays were analyzed. The sample essays were found to be predominantly deductive in terms of rhetorical pattern. Moreover, they mainly utilized ‘data’ and ‘claim’ most frequently with secondary elements of argument (i.e., counterargument claim, counterargument data, rebuttal claim, and rebuttal data) as the least produced elements. Overall writing quality co-varied significantly positively with the uses of claims, data, counterargument claims, counterargument data, rebuttal claims, and rebuttal data. Essays rated high in terms of overall writing quality were further rated for soundness and relevance of the arguments. The results demonstrate that even for advanced language learners good surface structure cannot necessarily guarantee well thought-out logical structure. The pedagogical implications for writing instruction and research are discussed

Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge

Despite the huge success of reinforcement learning (RL) in solving many difficult problems, its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking advantage of prior knowledge, intentionally or unintentionally, has usually been avoided, so that, training an agent from scratch is common. This not only causes sample inefficiency but also endangers safety –especially during exploration. In this paper, we help the agent learn from the environment by using the pre-existing (but not necessarily exact or complete) solution for a task. Our proposed method can be integrated with any RL algorithm developed based on policy gradient and actor-critic methods. The results on five tasks with different difficulty levels by using two well-known actor-critic-based methods as the backbone of our proposed method (SAC and TD3) show our success in greatly improving sample efficiency and final performance. We have gained these results alongside robustness to noisy environments at the cost of just a slight computational overhead, which is negligible

SWP-LeafNET : A novel multistage approach for plant leaf identification based on deep CNN

Modern scientific and technological advances allow botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the automated identification of plant species, a serious challenge due to variations in leaf morphology, including its size, texture, shape, and venation. Researchers have recently become more inclined toward deep learning-based methods rather than conventional feature-based methods due to the popularity and successful implementation of deep learning methods in image analysis, object recognition, and speech recognition. In this paper, to have an interpretable and reliable system, a botanist’s behavior is modeled in leaf identification by proposing a highly-efficient method of maximum behavioral resemblance developed through three deep learning-based models. Different layers of the three models are visualized to ensure that the botanist’s behavior is modeled accurately. The first and second models are designed from scratch. Regarding the third model, the pre-trained architecture MobileNetV2 is employed along with the transfer-learning technique. The proposed method is evaluated on two well-known datasets: Flavia and MalayaKew. According to a comparative analysis, the suggested approach is more accurate than hand-crafted feature extraction methods and other deep learning techniques in terms of 99.67% and 99.81% accuracy. Unlike conventional techniques that have their own specific complexities and depend on datasets, the proposed method requires no hand-crafted feature extraction. Also, it increases accuracy as compared with other deep learning techniques. Moreover, SWP-LeafNET is distributable and considerably faster than other methods because of using shallower models with fewer parameters asynchronously

Detection of efflux pump genes conferring multidrug resistance in clinical isolates of Acinetobacter Baumannii in Tehran province

Objective: Acinetobacter baumannii is among the most common bacterial agents causing nosocomial infections in the world. In recent years, the antibiotic resistance of A. baumannii strains has shown an increasing trend, which may be resulted from the activity of efflux pumps. This study was carried out to determine the efflux pump genes associated with MDR in clinical isolates of A. baumannii in Tehran province. Methods: In this study, 200 clinical samples were collected, and were identified through standard biochemical tests. Then, for the selected antibiotics, the antibacterial susceptibility patterns were determined using disk diffusion method with and without inhibitors of efflux pumps of add adeH, adeB, adeG, adeF, and adeS that were determined by employing PCR according to the Clinical & Laboratory Standards Institute (CLSI 2020) guideline. Results: A total of 60 clinical isolates of A. baumannii were identified and later confirmed by the detection of blaOXA-51-like and 16S rRNA genes. The findings of this study show that 98.37 of A. baumannii isolates were 100 resistant against piperacillin, meropenem, cefotaxime, ceftriaxone, ceftazidime, ceftazidime, and ciprofloxaci n. In addition, 100 of all A. baumannii isolates possessed AdeFBJ, 95 adeH, 80 adeS, and 76.7 adeG efflux pumps. Conclusion: The majority of A. baumannii isolates had antibiotic efflux pumps, and more than 73 of A. baumannii isolates were indicated to be resistant to the target antibiotics, indicating the significant role of efflux pumps in the development of resistance against these antibiotics. Copyright (C) 2021 Wolters Kluwer Health, Inc. All rights reserved